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General General

Bootstrapping random forest and CHAID for prediction of white spot disease among shrimp farmers.

In Scientific reports ; h5-index 158.0

Technology is playing an important role is healthcare particularly as it relates to disease prevention and detection. This is evident in the COVID-19 era as different technologies were deployed to test, detect and track patients and ensure COVID-19 protocol compliance. The White Spot Disease (WSD) is a very contagious disease caused by virus. It is widespread among shrimp farmers due to its mode of transmission and source. Considering the growing concern about the severity of the disease, this study provides a predictive model for diagnosis and detection of WSD among shrimp farmers using visualization and machine learning algorithms. The study made use of dataset from Mendeley repository. Machine learning algorithms; Random Forest classification and CHAID were applied for the study, while Python was used for implementation of algorithms and for visualization of results. The results achieved showed high prediction accuracy (98.28%) which is an indication of the suitability of the model for accurate prediction of the disease. The study would add to growing knowledge about use of technology to manage White Spot Disease among shrimp farmers and ensure real-time prediction during and post COVID-19.

Edeh Michael Onyema, Dalal Surjeet, Obagbuwa Ibidun Christiana, Prasad B V V Siva, Ninoria Shalini Zanzote, Wajid Mohd Anas, Adesina Ademola Olusola

2022-Dec-03

General General

COVID-DSNet: A novel deep convolutional neural network for detection of coronavirus (SARS-CoV-2) cases from CT and Chest X-Ray images.

In Artificial intelligence in medicine ; h5-index 34.0

COVID-19 (SARS-CoV-2), which causes acute respiratory syndrome, is a contagious and deadly disease that has devastating effects on society and human life. COVID-19 can cause serious complications, especially in patients with pre-existing chronic health problems such as diabetes, hypertension, lung cancer, weakened immune systems, and the elderly. The most critical step in the fight against COVID-19 is the rapid diagnosis of infected patients. Computed Tomography (CT), chest X-ray (CXR), and RT-PCR diagnostic kits are frequently used to diagnose the disease. However, due to difficulties such as the inadequacy of RT-PCR test kits and false negative (FN) results in the early stages of the disease, the time-consuming examination of medical images obtained from CT and CXR imaging techniques by specialists/doctors, and the increasing workload on specialists, it is challenging to detect COVID-19. Therefore, researchers have suggested searching for new methods in COVID- 19 detection. In analysis studies with CT and CXR radiography images, it was determined that COVID-19-infected patients experienced abnormalities related to COVID-19. The anomalies observed here are the primary motivation for artificial intelligence researchers to develop COVID-19 detection applications with deep convolutional neural networks. Here, convolutional neural network-based deep learning algorithms from artificial intelligence technologies with high discrimination capabilities can be considered as an alternative approach in the disease detection process. This study proposes a deep convolutional neural network, COVID-DSNet, to diagnose typical pneumonia (bacterial, viral) and COVID-19 diseases from CT, CXR, hybrid CT + CXR images. In the multi-classification study with the CT dataset, 97.60 % accuracy and 97.60 % sensitivity values were obtained from the COVID-DSNet model, and 100 %, 96.30 %, and 96.58 % sensitivity values were obtained in the detection of typical, common pneumonia and COVID-19, respectively. The proposed model is an economical, practical deep learning network that data scientists can benefit from and develop. Although it is not a definitive solution in disease diagnosis, it may help experts as it produces successful results in detecting pneumonia and COVID-19.

Reis Hatice Catal, Turk Veysel

2022-Dec

COVID-DSNet, Chest CT-scan images, Chest X-ray images, Depthwise separable convolution, SARS-CoV-2

General General

Deep variational graph autoencoders for novel host-directed therapy options against COVID-19.

In Artificial intelligence in medicine ; h5-index 34.0

The COVID-19 pandemic has been keeping asking urgent questions with respect to therapeutic options. Existing drugs that can be repurposed promise rapid implementation in practice because of their prior approval. Conceivably, there is still room for substantial improvement, because most advanced artificial intelligence techniques for screening drug repositories have not been exploited so far. We construct a comprehensive network by combining year-long curated drug-protein/protein-protein interaction data on the one hand, and most recent SARS-CoV-2 protein interaction data on the other hand. We learn the structure of the resulting encompassing molecular interaction network and predict missing links using variational graph autoencoders (VGAEs), as a most advanced deep learning technique that has not been explored so far. We focus on hitherto unknown links between drugs and human proteins that play key roles in the replication cycle of SARS-CoV-2. Thereby, we establish novel host-directed therapy (HDT) options whose utmost plausibility is confirmed by realistic simulations. As a consequence, many of the predicted links are likely to be crucial for the virus to thrive on the one hand, and can be targeted with existing drugs on the other hand.

Ray Sumanta, Lall Snehalika, Mukhopadhyay Anirban, Bandyopadhyay Sanghamitra, Schönhuth Alexander

2022-Dec

COVID-19, Host directed therapy, Molecular interaction network, Node2Vec, Variational graph autoEncoder

General General

Ubiquitous and smart healthcare monitoring frameworks based on machine learning: A comprehensive review.

In Artificial intelligence in medicine ; h5-index 34.0

During the COVID-19 pandemic, the patient care delivery paradigm rapidly shifted to remote technological solutions. Rising rates of life expectancy of older people, and deaths due to chronic diseases (CDs) such as cancer, diabetes and respiratory disease pose many challenges to healthcare. While the feasibility of Remote Patient Monitoring (RPM) with a Smart Healthcare Monitoring (SHM) framework was somewhat questionable before the COVID-19 pandemic, it is now a proven commodity and is on its way to becoming ubiquitous. More health organizations are adopting RPM to enable CD management in the absence of individual monitoring. The current studies on SHM have reviewed the applications of IoT and/or Machine Learning (ML) in the domain, their architecture, security, privacy and other network related issues. However, no study has analyzed the AI and ubiquitous computing advances in SHM frameworks. The objective of this research is to identify and map key technical concepts in the SHM framework. In this context an interesting and meaningful classification of the research articles surveyed for this work is presented. The comprehensive and systematic review is based on the "Preferred Reporting Items for Systematic Review and Meta-Analysis" (PRISMA) approach. A total of 2540 papers were screened from leading research archives from 2016 to March 2021, and finally, 50 articles were selected for review. The major advantages, developments, distinctive architectural structure, components, technical challenges and possibilities in SHM are briefly discussed. A review of various recent cloud and fog computing based architectures, major ML implementation challenges, prospects and future trends is also presented. The survey primarily encourages the data driven predictive analytics aspects of healthcare and the development of ML models for health empowerment.

Motwani Anand, Shukla Piyush Kumar, Pawar Mahesh

2022-Dec

Big data, Chronic diseases, Cloud computing, Cognitive computing, Data analytics, Edge computing, Internet-of-things, Machine learning, Remote patient monitoring, Smart healthcare monitoring, Ubiquitous computing

General General

Predicting depression and anxiety of Chinese population during COVID-19 in psychological evaluation data by XGBoost.

In Journal of affective disorders ; h5-index 79.0

BACKGROUND : Due to the onset of sudden stress, COVID-19 has greatly impacted the incidence of depression and anxiety. However, challenges still exist in identifying high-risk groups for depression and anxiety during COVID-19. Studies have identified how resilience and social support can be employed as effective predictors of depression and anxiety. This study aims to select the best combination of variables from measures of resilience, social support, and alexithymia for predicting depression and anxiety.

METHODS : The eXtreme Gradient Boosting (XGBoost1) model was applied to a dataset including data on 29,841 participants that was collected during the COVID-19 pandemic. Discriminant analyses on groups of participants with depression (DE2), anxiety (AN3), comorbid depression and anxiety (DA4), and healthy controls (HC5), were performed. All variables were selected according to their importance for classification. Further, analyses were performed with selected features to determine the best variable combination.

RESULTS : The mean accuracies achieved by three classification tasks, DE vs HC, AN vs HC, and DA vs HC, were 0.78, 0.77, and 0.89. Further, the combination of 19 selected features almost exhibited the same performance as all 56 variables (accuracies = 0.75, 0.75, and 0.86).

CONCLUSIONS : Resilience, social support, and some demographic data can accurately distinguish DE, AN, and DA from HC. The results can be used to inform screening practices for depression and anxiety. Additionally, the model performance of a limited scale including only 19 features indicates that using a simplified scale is feasible.

Tian Zhanxiao, Qu Wei, Zhao Yanli, Zhu Xiaolin, Wang Zhiren, Tan Yunlong, Jiang Ronghuan, Tan Shuping

2022-Nov-30

Anxiety, COVID-19 pandemic, Depression, Machine learning, Resilience, Social support

Ophthalmology Ophthalmology

Transforming ophthalmology in the digital century-new care models with added value for patients.

In Eye (London, England) ; h5-index 41.0

Ophthalmology faces many challenges in providing effective and meaningful eye care to an ever-increasing group of people. Even health systems that have so far been able to cope with the quantitative patient increase, due to their funding and the availability of highly qualified professionals, and improvements in practice routine efficiency, will be pushed to their limits. Further pressure on care will also be caused by new active substances for the largest group of patients with AMD, the so-called dry form. Treatment availability for this so far untreated group will increase the volume of patients 2-3 times. Without the adaptation of the care structures, this quantitative and qualitative expansion in therapy will inevitably lead to an undersupply.There is increasing scientific evidence that significant efficiency gains in the care of chronic diseases can be achieved through better networking of stakeholders in the healthcare system and greater patient involvement. Digitalization can make an important contribution here. Many technological solutions have been developed in recent years and the time is now ready to exploit this potential. The exceptional setting during the SARS-CoV-2 pandemic has shown many that new technology is available safely, quickly, and effectively. The emergency has catalyzed innovation processes and shown for post-pandemic time after that we are equipped to tackle the challenges in ophthalmic healthcare - ultimately for the benefit of patients and society.

Faes Livia, Maloca Peter M, Hatz Katja, Wolfensberger Thomas J, Munk Marion R, Sim Dawn A, Bachmann Lucas M, Schmid Martin K

2022-Dec-03

Radiology Radiology

Classification and visual explanation for COVID-19 pneumonia from CT images using triple learning.

In Scientific reports ; h5-index 158.0

This study presents a novel framework for classifying and visualizing pneumonia induced by COVID-19 from CT images. Although many image classification methods using deep learning have been proposed, in the case of medical image fields, standard classification methods are unable to be used in some cases because the medical images that belong to the same category vary depending on the progression of the symptoms and the size of the inflamed area. In addition, it is essential that the models used be transparent and explainable, allowing health care providers to trust the models and avoid mistakes. In this study, we propose a classification method using contrastive learning and an attention mechanism. Contrastive learning is able to close the distance for images of the same category and generate a better feature space for classification. An attention mechanism is able to emphasize an important area in the image and visualize the location related to classification. Through experiments conducted on two-types of classification using a three-fold cross validation, we confirmed that the classification accuracy was significantly improved; in addition, a detailed visual explanation was achieved comparison with conventional methods.

Kato Sota, Oda Masahiro, Mori Kensaku, Shimizu Akinobu, Otake Yoshito, Hashimoto Masahiro, Akashi Toshiaki, Hotta Kazuhiro

2022-Dec-02

General General

A lightweight network for COVID-19 detection in X-ray images.

In Methods (San Diego, Calif.)

The Novel Coronavirus 2019 (COVID-19) is a global pandemic which has a devastating impact. Due to its quick transmission, a prominent challenge in confronting this pandemic is the rapid diagnosis. Currently, the commonly-used diagnosis is the specific molecular tests aided with the medical imaging modalities such as chest X-ray (CXR). However, with the large demand, the diagnoses of CXR are time-consuming and laborious. Deep learning is promising for automatically diagnosing COVID-19 to ease the burden on medical systems. At present, the most applied neural networks are large, which hardly satisfy the rapid yet inexpensive requirements of COVID-19 detection. To reduce huge computation and memory demands, in this paper, we focus on implementing lightweight networks for COVID-19 detection in CXR. Concretely, we first augment data based on clinical visual features of CXR from expertise. Then, according to the fact that all the input data are CXR, we design a targeted four-layer network with either 11×11 or 3×3 kernels to recognize regional features and detail features. A pruning criterion based on the weights importance is also proposed to further prune the network. Experiments on a public COVID-19 dataset validate the effectiveness and efficiency of the proposed method.

Shi Yong, Tang Anda, Xiao Yang, Niu Lingfeng

2022-Nov-29

COVID-19 detection, network pruning, neural network

General General

Res-SE-ConvNet: A Deep Neural Network for Hypoxemia Severity Prediction for Hospital In-Patients Using Photoplethysmograph Signal.

In IEEE journal of translational engineering in health and medicine

Determining the severity level of hypoxemia, the scarcity of saturated oxygen (SpO2) in the human body, is very important for the patients, a matter which has become even more significant during the outbreak of Covid-19 variants. Although the widespread usage of Pulse Oximeter has helped the doctors aware of the current level of SpO2 and thereby determine the hypoxemia severity of a particular patient, the high sensitivity of the device can lead to the desensitization of the care-givers, resulting in slower response to actual hypoxemia event. There has been research conducted for the detection of severity level using various parameters and bio-signals and feeding them in a machine learning algorithm. However, in this paper, we have proposed a new residual-squeeze-excitation-attention based convolutional network (Res-SE-ConvNet) using only Photoplethysmography (PPG) signal for the comfortability of the patient. Unlike the other methods, the proposed method has outperformed the standard state-of-art methods as the result shows 96.5% accuracy in determining 3 class severity problems with 0.79 Cohen Kappa score. This method has the potential to aid the patients in receiving the benefit of an automatic and faster clinical decision support system, thus handling the severity of hypoxemia.

Mahmud Talha Ibn, Imran Sheikh Asif, Shahnaz Celia

2022

Saturated oxygen, attention, deep learning, excitation, feature map

General General

Analysis of the effect of an artificial intelligence chatbot educational program on non-face-to-face classes: a quasi-experimental study.

In BMC medical education

BACKGROUND : Education and training are needed for nursing students using artificial intelligence-based educational programs. However, few studies have assessed the effect of using chatbots in nursing education.

OBJECTIVES : This study aimed to develop and examine the effect of an artificial intelligence chatbot educational program for promoting nursing skills related to electronic fetal monitoring in nursing college students during non-face-to-face classes during the COVID-19 pandemic.

DESIGN : This quasi-experimental study used a nonequivalent control group non-synchronized pretest-posttest design.

METHODS : The participants were 61 junior students from a nursing college located in G province of South Korea. Data were collected between November 3 and 16, 2021, and analyzed using independent t-tests.

RESULTS : The experimental group-in which the artificial intelligence chatbot program was applied-did not show statistically significant differences in knowledge (t = -0.58, p = .567), clinical reasoning competency (t = 0.75, p = .455), confidence (t = 1.13, p = .264), and feedback satisfaction (t = 1.72, p = .090), compared with the control group; however, its participants' interest in education (t = 2.38, p = .020) and self-directed learning (t = 2.72, p = .006) were significantly higher than those in the control group.

CONCLUSION : The findings of our study highlighted the potential of artificial intelligence chatbot programs as an educational assistance tool to promote nursing college students' interest in education and self-directed learning. Moreover, such programs can be effective in enhancing nursing students' skills in non-face-to face-situations caused by the ongoing COVID-19 pandemic.

Han Jeong-Won, Park Junhee, Lee Hanna

2022-Dec-01

Artificial intelligence, Chatbot program, Clinical reasoning, Data processing, Education, Nursing

Public Health Public Health

Data-driven identification of post-acute SARS-CoV-2 infection subphenotypes.

In Nature medicine ; h5-index 170.0

The post-acute sequelae of SARS-CoV-2 infection (PASC) refers to a broad spectrum of symptoms and signs that are persistent, exacerbated or newly incident in the period after acute SARS-CoV-2 infection. Most studies have examined these conditions individually without providing evidence on co-occurring conditions. In this study, we leveraged the electronic health record data of two large cohorts, INSIGHT and OneFlorida+, from the national Patient-Centered Clinical Research Network. We created a development cohort from INSIGHT and a validation cohort from OneFlorida+ including 20,881 and 13,724 patients, respectively, who were SARS-CoV-2 infected, and we investigated their newly incident diagnoses 30-180 days after a documented SARS-CoV-2 infection. Through machine learning analysis of over 137 symptoms and conditions, we identified four reproducible PASC subphenotypes, dominated by cardiac and renal (including 33.75% and 25.43% of the patients in the development and validation cohorts); respiratory, sleep and anxiety (32.75% and 38.48%); musculoskeletal and nervous system (23.37% and 23.35%); and digestive and respiratory system (10.14% and 12.74%) sequelae. These subphenotypes were associated with distinct patient demographics, underlying conditions before SARS-CoV-2 infection and acute infection phase severity. Our study provides insights into the heterogeneity of PASC and may inform stratified decision-making in the management of PASC conditions.

Zhang Hao, Zang Chengxi, Xu Zhenxing, Zhang Yongkang, Xu Jie, Bian Jiang, Morozyuk Dmitry, Khullar Dhruv, Zhang Yiye, Nordvig Anna S, Schenck Edward J, Shenkman Elizabeth A, Rothman Russell L, Block Jason P, Lyman Kristin, Weiner Mark G, Carton Thomas W, Wang Fei, Kaushal Rainu

2022-Dec-01

Public Health Public Health

Modeling approaches for early warning and monitoring of pandemic situations as well as decision support.

In Frontiers in public health

The COVID-19 pandemic has highlighted the lack of preparedness of many healthcare systems against pandemic situations. In response, many population-level computational modeling approaches have been proposed for predicting outbreaks, spatiotemporally forecasting disease spread, and assessing as well as predicting the effectiveness of (non-) pharmaceutical interventions. However, in several countries, these modeling efforts have only limited impact on governmental decision-making so far. In light of this situation, the review aims to provide a critical review of existing modeling approaches and to discuss the potential for future developments.

Botz Jonas, Wang Danqi, Lambert Nicolas, Wagner Nicolas, Génin Marie, Thommes Edward, Madan Sumit, Coudeville Laurent, Fröhlich Holger

2022

agent-based-modeling, artificial intelligence, compartmental models, machine learning, pandemic

Public Health Public Health

Symptom diaries as a digital tool to detect SARS-CoV-2 infections and differentiate between prevalent variants.

In Frontiers in public health

The COVID-19 pandemic and the high numbers of infected individuals pose major challenges for public health departments. To overcome these challenges, the health department in Cologne has developed a software called DiKoMa. This software offers the possibility to track contact and index persons, but also provides a digital symptom diary. In this work, the question of whether these can also be used for diagnostic purposes will be investigated. Machine learning makes it possible to identify infections based on early symptom profiles and to distinguish between the predominant dominant variants. Focusing on the occurrence of the symptoms in the first week, a decision tree is trained for the differentiation between contact and index persons and the prevailing dominant variants (Wildtype, Alpha, Delta, and Omicron). The model is evaluated, using sex- and age-stratified cross-validation and validated by symptom profiles of the first 6 days. The variants achieve an AUC-ROC from 0.89 for Omicron and 0.6 for Alpha. No significant differences are observed for the results of the validation set (Alpha 0.63 and Omicron 0.87). The evaluation of symptom combinations using artificial intelligence can determine the individual risk for the presence of a COVID-19 infection, allows assignment to virus variants, and can contribute to the management of epidemics and pandemics on a national and international level. It can help to reduce the number of specific tests in times of low labor capacity and could help to early identify new virus variants.

Grüne Barbara, Kugler Sabine, Ginzel Sebastian, Wolff Anna, Buess Michael, Kossow Annelene, Küfer-Weiß Annika, Rüping Stefan, Neuhann Florian

2022

SARS-CoV-2, classification, digital symptom diaries, health department, machine learning, prevalent virus variants, symptom combinations

Pathology Pathology

Computational approaches for network-based integrative multi-omics analysis.

In Frontiers in molecular biosciences

Advances in omics technologies allow for holistic studies into biological systems. These studies rely on integrative data analysis techniques to obtain a comprehensive view of the dynamics of cellular processes, and molecular mechanisms. Network-based integrative approaches have revolutionized multi-omics analysis by providing the framework to represent interactions between multiple different omics-layers in a graph, which may faithfully reflect the molecular wiring in a cell. Here we review network-based multi-omics/multi-modal integrative analytical approaches. We classify these approaches according to the type of omics data supported, the methods and/or algorithms implemented, their node and/or edge weighting components, and their ability to identify key nodes and subnetworks. We show how these approaches can be used to identify biomarkers, disease subtypes, crosstalk, causality, and molecular drivers of physiological and pathological mechanisms. We provide insight into the most appropriate methods and tools for research questions as showcased around the aetiology and treatment of COVID-19 that can be informed by multi-omics data integration. We conclude with an overview of challenges associated with multi-omics network-based analysis, such as reproducibility, heterogeneity, (biological) interpretability of the results, and we highlight some future directions for network-based integration.

Agamah Francis E, Bayjanov Jumamurat R, Niehues Anna, Njoku Kelechi F, Skelton Michelle, Mazandu Gaston K, Ederveen Thomas H A, Mulder Nicola, Chimusa Emile R, ‘t Hoen Peter A C

2022

data integration, machine learning, multi-modal network, multi-omics, network causal inference, network diffusion/propagation

General General

Radiomorphological signs and clinical severity of SARS-CoV-2 lineage B.1.1.7.

In BJR open

OBJECTIVE : We aimed to assess the differences in the severity and chest-CT radiomorphological signs of SARS-CoV-2 B.1.1.7 and non-B.1.1.7 variants.

METHODS : We collected clinical data of consecutive patients with laboratory-confirmed COVID-19 and chest-CT imaging who were admitted to the Emergency Department between September 1- November 13, 2020 (non-B.1.1.7 cases) and March 1-March 18, 2021 (B.1.1.7 cases). We also examined the differences in the severity and radiomorphological features associated with COVID-19 pneumonia. Total pneumonia burden (%), mean attenuation of ground-glass opacities and consolidation were quantified using deep-learning research software.

RESULTS : The final population comprised 500 B.1.1.7 and 500 non-B.1.1.7 cases. Patients with B.1.1.7 infection were younger (58.5 ± 15.6 vs 64.8 ± 17.3; p < .001) and had less comorbidities. Total pneumonia burden was higher in the B.1.1.7 patient group (16.1% [interquartile range (IQR):6.0-34.2%] vs 6.6% [IQR:1.2-18.3%]; p < .001). In the age-specific analysis, in patients <60 years B.1.1.7 pneumonia had increased consolidation burden (0.1% [IQR:0.0-0.7%] vs 0.1% [IQR:0.0-0.2%]; p < .001), and severe COVID-19 was more prevalent (11.5% vs  4.9%; p = .032). Mortality rate was similar in all age groups.

CONCLUSION : Despite B.1.1.7 patients were younger and had fewer comorbidities, they experienced more severe disease than non-B.1.1.7 patients, however, the risk of death was the same between the two groups.

ADVANCES IN KNOWLEDGE : Our study provides data on deep-learning based quantitative lung lesion burden and clinical outcomes of patients infected by B.1.1.7 VOC. Our findings might serve as a model for later investigations, as new variants are emerging across the globe.

Simon Judit, Grodecki Kajetan, Cadet Sebastian, Killekar Aditya, Slomka Piotr, Zara Samuel James, Zsarnóczay Emese, Nardocci Chiara, Nagy Norbert, Kristóf Katalin, Vásárhelyi Barna, Müller Veronika, Merkely Béla, Dey Damini, Maurovich-Horvat Pál

2022

General General

Next-generation proteomics of serum extracellular vesicles combined with single-cell RNA sequencing identifies MACROH2A1 associated with refractory COVID-19.

In Inflammation and regeneration

BACKGROUND : The coronavirus disease 2019 (COVID-19) pandemic is widespread; however, accurate predictors of refractory cases have not yet been established. Circulating extracellular vesicles, involved in many pathological processes, are ideal resources for biomarker exploration.

METHODS : To identify potential serum biomarkers and examine the proteins associated with the pathogenesis of refractory COVID-19, we conducted high-coverage proteomics on serum extracellular vesicles collected from 12 patients with COVID-19 at different disease severity levels and 4 healthy controls. Furthermore, single-cell RNA sequencing of peripheral blood mononuclear cells collected from 10 patients with COVID-19 and 5 healthy controls was performed.

RESULTS : Among the 3046 extracellular vesicle proteins that were identified, expression of MACROH2A1 was significantly elevated in refractory cases compared to non-refractory cases; moreover, its expression was increased according to disease severity. In single-cell RNA sequencing of peripheral blood mononuclear cells, the expression of MACROH2A1 was localized to monocytes and elevated in critical cases. Consistently, single-nucleus RNA sequencing of lung tissues revealed that MACROH2A1 was highly expressed in monocytes and macrophages and was significantly elevated in fatal COVID-19. Moreover, molecular network analysis showed that pathways such as "estrogen signaling pathway," "p160 steroid receptor coactivator (SRC) signaling pathway," and "transcriptional regulation by STAT" were enriched in the transcriptome of monocytes in the peripheral blood mononuclear cells and lungs, and they were also commonly enriched in extracellular vesicle proteomics.

CONCLUSIONS : Our findings highlight that MACROH2A1 in extracellular vesicles is a potential biomarker of refractory COVID-19 and may reflect the pathogenesis of COVID-19 in monocytes.

Kawasaki Takahiro, Takeda Yoshito, Edahiro Ryuya, Shirai Yuya, Nogami-Itoh Mari, Matsuki Takanori, Kida Hiroshi, Enomoto Takatoshi, Hara Reina, Noda Yoshimi, Adachi Yuichi, Niitsu Takayuki, Amiya Saori, Yamaguchi Yuta, Murakami Teruaki, Kato Yasuhiro, Morita Takayoshi, Yoshimura Hanako, Yamamoto Makoto, Nakatsubo Daisuke, Miyake Kotaro, Shiroyama Takayuki, Hirata Haruhiko, Adachi Jun, Okada Yukinori, Kumanogoh Atsushi

2022-Nov-30

COVID-19, Exosome, Liquid biopsy, MACROH2A1, Multi-omics, SARS-CoV-2

General General

Predicting Immune Escape with Pretrained Protein Language Model Embeddings

bioRxiv Preprint

Assessing the severity of new pathogenic variants requires an understanding of which mutations enable escape of the human immune response. Even single point mutations to an antigen can cause immune escape and infection by disrupting antibody binding. Recent work has modeled the effect of single point mutations on proteins by leveraging the information contained in large-scale, pretrained protein language models (PLMs). PLMs are often applied in a zero-shot setting, where the effect of each mutation is predicted based on the output of the language model with no additional training. However, this approach cannot appropriately model immune escape, which involves the interaction of two proteins--antibody and antigen--instead of one protein and requires making different predictions for the same antigenic mutation in response to different antibodies. Here, we explore several methods for predicting immune escape by building models on top of embeddings from PLMs. We evaluate our methods on a SARS-CoV-2 deep mutational scanning dataset and show that our embedding-based methods significantly outperform zero-shot methods, which have almost no predictive power. We also highlight insights gained into how best to use embeddings from PLMs to predict escape. Despite these promising results, simple statistical and machine learning baseline models that do not use pretraining perform comparably, showing that computationally expensive pretraining approaches may not be beneficial for escape prediction. Furthermore, all models perform relatively poorly, indicating that future work is necessary to improve escape prediction with or without pretrained embeddings.

Swanson, K.; Chang, H.; Zou, J.

2022-12-02

Radiology Radiology

Application of deep learning-based diagnostic systems in screening asymptomatic COVID-19 patients among oversea returnees.

In Journal of infection in developing countries

INTRODUCTION : Our study aimed to investigate the performance of deep learning (DL)-based diagnostic systems in alerting against COVID-19, especially among asymptomatic individuals coming from overseas, and to analyze the features of identified asymptomatic patients in detail.

METHODOLOGY : DL diagnostic systems were deployed to assist in the screening of COVID-19, including the pneumonia system and pulmonary nodules system. 1,917 overseas returnees who underwent CT examination and rRT-PCR tests were enrolled. DL pneumonia system promptly alerted clinicians to suspected COVID-19 after CT examinations while the performance was evaluated with rRT-PCR results as the reference. The radiological features of asymptomatic COVID-19 cases were described according to the Nomenclature of the Fleischner Society.

RESULTS : Fifty-three cases were confirmed as COVID-19 patients by rRT-PCR tests, including 5 asymptomatic cases. DL pneumonia system correctly alerted 50 cases as suspected COVID-19 with a sensitivity of 0.9434 and specificity of 0.9592 (within 2 minutes per case); while the pulmonary nodules system alerted 2 of the 3 missed asymptomatic cases. Additionally, five asymptomatic patients presented different characteristics such as elevated creatine kinase level and prolonged prothrombin time, as well as atypical radiological features.

CONCLUSIONS : DL diagnostic systems are promising complementary approaches for prompt screening of imported COVID-19 patients, even the imported asymptomatic cases. Unique clinical and radiological characteristics of asymptomatic cases might be of great value in screening as well.

ADVANCES IN KNOWLEDGE : DL-based systems are practical, efficient, and reliable to assist radiologists in screening COVID-19 patients. Differential features of asymptomatic patients might be useful to clinicians in the frontline to differentiate asymptomatic cases.

Dong Dawei, Luo Zujin, Zheng Yue, Liang Ying, Zhao Pengfei, Feng Linlin, Wang Dawei, Cao Ying, Zhao Zhenhao, Ma Yingmin

2022-Nov-29

COVID-19, Deep learning, asymptomatic cases, diagnostic systems, performance evaluation

General General

Multigroup prediction in lung cancer patients and comparative controls using signature of volatile organic compounds in breath samples.

In PloS one ; h5-index 176.0

Early detection of lung cancer is a crucial factor for increasing its survival rates among the detected patients. The presence of carbonyl volatile organic compounds (VOCs) in exhaled breath can play a vital role in early detection of lung cancer. Identifying these VOC markers in breath samples through innovative statistical and machine learning techniques is an important task in lung cancer research. Therefore, we proposed an experimental approach for generation of VOC molecular concentration data using unique silicon microreactor technology and further identification and characterization of key relevant VOCs important for lung cancer detection through statistical and machine learning algorithms. We reported several informative VOCs and tested their effectiveness in multi-group classification of patients. Our analytical results indicated that seven key VOCs, including C4H8O2, C13H22O, C11H22O, C2H4O2, C7H14O, C6H12O, and C5H8O, are sufficient to detect the lung cancer patients with higher mean classification accuracy (92%) and lower standard error (0.03) compared to other combinations. In other words, the molecular concentrations of these VOCs in exhaled breath samples were able to discriminate the patients with lung cancer (n = 156) from the healthy smoker and nonsmoker controls (n = 193) and patients with benign pulmonary nodules (n = 65). The quantification of carbonyl VOC profiles from breath samples and identification of crucial VOCs through our experimental approach paves the way forward for non-invasive lung cancer detection. Further, our experimental and analytical approach of VOC quantitative analysis in breath samples may be extended to other diseases, including COVID-19 detection.

Rai Shesh N, Das Samarendra, Pan Jianmin, Mishra Dwijesh C, Fu Xiao-An

2022

General General

Highly Adsorptive Au-TiO2 Nanocomposites for the SERS Face Mask Allow the Machine-Learning-Based Quantitative Assay of SARS-CoV-2 in Artificial Breath Aerosols.

In ACS applied materials & interfaces ; h5-index 147.0

Human respiratory aerosols contain diverse potential biomarkers for early disease diagnosis. Here, we report the direct and label-free detection of SARS-CoV-2 in respiratory aerosols using a highly adsorptive Au-TiO2 nanocomposite SERS face mask and an ablation-assisted autoencoder. The Au-TiO2 SERS face mask continuously preconcentrates and efficiently captures the oronasal aerosols, which substantially enhances the SERS signal intensities by 47% compared to simple Au nanoislands. The ultrasensitive Au-TiO2 nanocomposites also demonstrate the successful detection of SARS-CoV-2 spike proteins in artificial respiratory aerosols at a 100 pM concentration level. The deep learning-based autoencoder, followed by the partial ablation of nondiscriminant SERS features of spike proteins, allows a quantitative assay of the 101-104 pfu/mL SARS-CoV-2 lysates (comparable to 19-29 PCR cyclic threshold from COVID-19 patients) in aerosols with an accuracy of over 98%. The Au-TiO2 SERS face mask provides a platform for breath biopsy for the detection of various biomarkers in respiratory aerosols.

Hwang Charles S H, Lee Sangyeon, Lee Sejin, Kim Hanjin, Kang Taejoon, Lee Doheon, Jeong Ki-Hun

2022-Nov-30

SARS-CoV-2, breath biopsy, machine-learning, nanocomposite, plasmonics, surface-enhanced Raman spectroscopy

General General

Emerging 0D, 1D, 2D, and 3D nanostructures for efficient point-of-care biosensing.

In Biosensors & bioelectronics: X

The recent COVID-19 infection outbreak has raised the demand for rapid, highly sensitive POC biosensing technology for intelligent health and wellness. In this direction, efforts are being made to explore high-performance nano-systems for developing novel sensing technologies capable of functioning at point-of-care (POC) applications for quick diagnosis, data acquisition, and disease management. A combination of nanostructures [i.e., 0D (nanoparticles & quantum dots), 1D (nanorods, nanofibers, nanopillars, & nanowires), 2D (nanosheets, nanoplates, nanopores) & 3D nanomaterials (nanocomposites and complex hierarchical structures)], biosensing prototype, and micro-electronics makes biosensing suitable for early diagnosis, detection & prevention of life-threatening diseases. However, a knowledge gap associated with the potential of 0D, 1D, 2D, and 3D nanostructures for the design and development of efficient POC sensing is yet to be explored carefully and critically. With this focus, this review highlights the latest engineered 0D, 1D, 2D, and 3D nanomaterials for developing next-generation miniaturized, portable POC biosensors development to achieve high sensitivity with potential integration with the internet of medical things (IoMT, for miniaturization and data collection, security, and sharing), artificial intelligence (AI, for desired analytics), etc. for better diagnosis and disease management at the personalized level.

Byakodi Manisha, Shrikrishna Narlawar Sagar, Sharma Riya, Bhansali Shekhar, Mishra Yogendra, Kaushik Ajeet, Gandhi Sonu

2022-Nov-25

0D to 3D nanomaterials, Biosensors, Efficient diagnostics, Personalized health management, Point-of-care testing, Wearable

General General

Automatic detection of Covid-19 from chest X-ray and lung computed tomography images using deep neural networks and transfer learning.

In Applied soft computing

The world has been undergoing the most ever unprecedented circumstances caused by the coronavirus pandemic, which is having a devastating global effect in different aspects of life. Since there are not effective antiviral treatments for Covid-19 yet, it is crucial to early detect and monitor the progression of the disease, thereby helping to reduce mortality. While different measures are being used to combat the virus, medical imaging techniques have been examined to support doctors in diagnosing the disease. In this paper, we present a practical solution for the detection of Covid-19 from chest X-ray (CXR) and lung computed tomography (LCT) images, exploiting cutting-edge Machine Learning techniques. As the main classification engine, we make use of EfficientNet and MixNet, two recently developed families of deep neural networks. Furthermore, to make the training more effective and efficient, we apply three transfer learning algorithms. The ultimate aim is to build a reliable expert system to detect Covid-19 from different sources of images, making it be a multi-purpose AI diagnosing system. We validated our proposed approach using four real-world datasets. The first two are CXR datasets consist of 15,000 and 17,905 images, respectively. The other two are LCT datasets with 2,482 and 411,528 images, respectively. The five-fold cross-validation methodology was used to evaluate the approach, where the dataset is split into five parts, and accordingly the evaluation is conducted in five rounds. By each evaluation, four parts are combined to form the training data, and the remaining one is used for testing. We obtained an encouraging prediction performance for all the considered datasets. In all the configurations, the obtained accuracy is always larger than 95.0%. Compared to various existing studies, our approach yields a substantial performance gain. Moreover, such an improvement is statistically significant.

Duong Linh T, Nguyen Phuong T, Iovino Ludovico, Flammini Michele

2022-Nov-24

AI Diagnosis systems, COVID-19, Chest X-ray image, Expert systems, Lung CT images

General General

Contextual counters and multimodal Deep Learning for activity-level traffic classification of mobile communication apps during COVID-19 pandemic.

In Computer networks

The COVID-19 pandemic has reshaped Internet traffic due to the huge modifications imposed to lifestyle of people resorting more and more to collaboration and communication apps to accomplish daily tasks. Accordingly, these dramatic changes call for novel traffic management solutions to adequately countermeasure such unexpected and massive changes in traffic characteristics. In this paper, we focus on communication and collaboration apps whose traffic experienced a sudden growth during the last two years. Specifically, we consider nine apps whose traffic we collect, reliably label, and publicly release as a new dataset (MIRAGE-COVID-CCMA-2022) to the scientific community. First, we investigate the capability of state-of-art single-modal and multimodal Deep Learning-based classifiers in telling the specific app, the activity performed by the user, or both. While we highlight that state-of-art solutions reports a more-than-satisfactory performance in addressing app classification (96%-98% F-measure), evident shortcomings stem out when tackling activity classification (56%-65% F-measure) when using approaches that leverage the transport-layer payload and/or per-packet information attainable from the initial part of the biflows. In line with these limitations, we design a novel set of inputs (namely Context Inputs) providing clues about the nature of a biflow by observing the biflows coexisting simultaneously. Based on these considerations, we propose Mimetic-All a novel early traffic classification multimodal solution that leverages Context Inputs as an additional modality, achieving 82 % F-measure in activity classification. Also, capitalizing the multimodal nature of Mimetic-All, we evaluate different combinations of the inputs. Interestingly, experimental results witness that Mimetic-ConSeq-a variant that uses the Context Inputs but does not rely on payload information (thus gaining greater robustness to more opaque encryption sub-layers possibly going to be adopted in the future)-experiences only 1 % F-measure drop in performance w.r.t. Mimetic-All and results in a shorter training time.

Guarino Idio, Aceto Giuseppe, Ciuonzo Domenico, Montieri Antonio, Persico Valerio, Pescapè Antonio

2022-Dec-24

COVID-19, Collaboration apps, Communication apps, Contextual counters, Deep Learning, Encrypted traffic, Multimodal techniques, Traffic classification

General General

Supervised Machine Learning Approach to COVID-19 Detection Based on Clinical Data.

In Medical journal of the Islamic Republic of Iran

Background: The new coronavirus has been spreading since the beginning of 2020, and many efforts have been made to develop vaccines to help patients recover. It is now clear that the world needs a rapid solution to curb the spread of COVID-19 worldwide with non-clinical approaches such as artificial intelligence techniques. These approaches can be effective in reducing the burden on the health care system to provide the best possible way to diagnose the COVID-19 epidemic. This study was conducted to use Machine Learning (ML) algorithms for the early detection of COVID-19 in patients. Methods: This retrospective study used data from hospitals affiliated with Shiraz University of Medical Sciences in Iran. This dataset was collected in the period March to October 2020 andcontained 10055 cases with 63 features. We selected and compared six algorithms: C4.5, support vector machine (SVM), Naive Bayes, logistic Regression (LR), Random Forest, and K-Nearest Neighbor algorithm using Rapid Miner software. The performance of algorithms was measured using evaluation metrics, such as precision, recall, accuracy, and f-measure. Results: The results of the study show that among the various used classification methods in the diagnosis of coronavirus, SVM (93.41% accuracy) and C4.5 (91.87% accuracy) achieved the highest performance. According to the C4.5 decision tree, "contact with a person who has COVID-19" was considered the most important diagnostic criterion based on the Gini index. Conclusion: We found that ML approaches enable a reasonable level of accuracy in the diagnosis of COVID-19.

Yazdani Azita, Zahmatkeshan Maryam, Ravangard Ramin, Sharifian Roxana, Shirdeli Mohammad

2022

Artificial Intelligence, COVID-19, Classification, Data mining, Machine Learning

General General

Disease-related compound identification based on deeping learning method.

In Scientific reports ; h5-index 158.0

Acute lung injury (ALI) is a serious respiratory disease, which can lead to acute respiratory failure or death. It is closely related to the pathogenesis of New Coronavirus pneumonia (COVID-19). Many researches showed that traditional Chinese medicine (TCM) had a good effect on its intervention, and network pharmacology could play a very important role. In order to construct "disease-gene-target-drug" interaction network more accurately, deep learning algorithm is utilized in this paper. Two ALI-related target genes (REAL and SATA3) are considered, and the active and inactive compounds of the two corresponding target genes are collected as training data, respectively. Molecular descriptors and molecular fingerprints are utilized to characterize each compound. Forest graph embedded deep feed forward network (forgeNet) is proposed to train. The experimental results show that forgeNet performs better than support vector machines (SVM), random forest (RF), logical regression (LR), Naive Bayes (NB), XGBoost, LightGBM and gcForest. forgeNet could identify 19 compounds in Erhuang decoction (EhD) and Dexamethasone (DXMS) more accurately.

Yang Bin, Bao Wenzheng, Wang Jinglong, Chen Baitong, Iwamori Naoki, Chen Jiazi, Chen Yuehui

2022-Nov-29

Internal Medicine Internal Medicine

The prognostic utility of serum thyrotropin in hospitalized Covid-19 patients: statistical and machine learning approaches.

In Endocrine

PURPOSE : To assess the prognostic value of serum TSH in Greek patients with COVID-19 and compare it with that of commonly used prognostic biomarkers.

METHODS : Retrospective study of 128 COVID-19 in patients with no history of thyroid disease. Serum TSH, albumin, CRP, ferritin, and D-dimers were measured at admission. Outcomes were classified as "favorable" (discharge from hospital) and "adverse" (intubation or in-hospital death of any cause). The prognostic performance of TSH and other indices was assessed using binary logistic regression, machine learning classifiers, and ROC curve analysis.

RESULTS : Patients with adverse outcomes had significantly lower TSH compared to those with favorable outcomes (0.61 versus 1.09 mIU/L, p < 0.001). Binary logistic regression with sex, age, TSH, albumin, CRP, ferritin, and D-dimers as covariates showed that only albumin (p < 0.001) and TSH (p = 0.006) were significantly predictive of the outcome. Serum TSH below the optimal cut-off value of 0.5 mIU/L was associated with an odds ratio of 4.13 (95% C.I.: 1.41-12.05) for adverse outcome. Artificial neural network analysis showed that the prognostic importance of TSH was second only to that of albumin. However, the prognostic accuracy of low TSH was limited, with an AUC of 69.5%, compared to albumin's 86.9%. A Naïve Bayes classifier based on the combination of serum albumin and TSH levels achieved high prognostic accuracy (AUC 99.2%).

CONCLUSION : Low serum TSH is independently associated with adverse outcome in hospitalized Greek patients with COVID-19 but its prognostic utility is limited. The integration of serum TSH into machine learning classifiers in combination with other biomarkers enables outcome prediction with high accuracy.

Pappa E, Gourna P, Galatas G, Manti M, Romiou A, Panagiotou L, Chatzikyriakou R, Trakas N, Feretzakis G, Christopoulos C

2022-Nov-29

Artificial intelligence, Bayes classifier, COVID-19, Machine learning, Non-thyroidal illness syndrome, Thyroid stimulating hormone

General General

Automatic Detection of Twitter Users Who Express Chronic Stress Experiences via Supervised Machine Learning and Natural Language Processing.

In Computers, informatics, nursing : CIN

Americans bear a high chronic stress burden, particularly during the COVID-19 pandemic. Although social media have many strengths to complement the weaknesses of conventional stress measures, including surveys, they have been rarely utilized to detect individuals self-reporting chronic stress. Thus, this study aimed to develop and evaluate an automatic system on Twitter to identify users who have self-reported chronic stress experiences. Using the Twitter public streaming application programming interface, we collected tweets containing certain stress-related keywords (eg, "chronic," "constant," "stress") and then filtered the data using pre-defined text patterns. We manually annotated tweets with (without) self-report of chronic stress as positive (negative). We trained multiple classifiers and tested them via accuracy and F1 score. We annotated 4195 tweets (1560 positives, 2635 negatives), achieving an inter-annotator agreement of 0.83 (Cohen's kappa). The classifier based on Bidirectional Encoder Representation from Transformers performed the best (accuracy of 83.6% [81.0-86.1]), outperforming the second best-performing classifier (support vector machines: 76.4% [73.5-79.3]). The past tweets from the authors of positive tweets contained useful information, including sources and health impacts of chronic stress. Our study demonstrates that users' self-reported chronic stress experiences can be automatically identified on Twitter, which has a high potential for surveillance and large-scale intervention.

Yang Yuan-Chi, Xie Angel, Kim Sangmi, Hair Jessica, Al-Garadi Mohammed, Sarker Abeed

2022-Nov-28

General General

On the Design of Communication-Efficient Federated Learning for Health Monitoring

ArXiv Preprint

With the booming deployment of Internet of Things, health monitoring applications have gradually prospered. Within the recent COVID-19 pandemic situation, interest in permanent remote health monitoring solutions has raised, targeting to reduce contact and preserve the limited medical resources. Among the technological methods to realize efficient remote health monitoring, federated learning (FL) has drawn particular attention due to its robustness in preserving data privacy. However, FL can yield to high communication costs, due to frequent transmissions between the FL server and clients. To tackle this problem, we propose in this paper a communication-efficient federated learning (CEFL) framework that involves clients clustering and transfer learning. First, we propose to group clients through the calculation of similarity factors, based on the neural networks characteristics. Then, a representative client in each cluster is selected to be the leader of the cluster. Differently from the conventional FL, our method performs FL training only among the cluster leaders. Subsequently, transfer learning is adopted by the leader to update its cluster members with the trained FL model. Finally, each member fine-tunes the received model with its own data. To further reduce the communication costs, we opt for a partial-layer FL aggregation approach. This method suggests partially updating the neural network model rather than fully. Through experiments, we show that CEFL can save up to to 98.45% in communication costs while conceding less than 3% in accuracy loss, when compared to the conventional FL. Finally, CEFL demonstrates a high accuracy for clients with small or unbalanced datasets.

Dong Chu, Wael Jaafar, Halim Yanikomeroglu

2022-11-30

General General

Reactive-diffusion epidemic model on human mobility networks: Analysis and applications to COVID-19 in China.

In Physica A

The complex dynamics of human mobility, combined with sporadic cases of local outbreaks, make assessing the impact of large-scale social distancing on COVID-19 propagation in China a challenge. In this paper, with the travel big dataset supported by Baidu migration platform, we develop a reactive-diffusion epidemic model on human mobility networks to characterize the spatio-temporal propagation of COVID-19, and a novel time-dependent function is incorporated into the model to describe the effects of human intervention. By applying the system control theory, we discuss both constant and time-varying threshold behavior of proposed model. In the context of population mobility-mediated epidemics in China, we explore the transmission patterns of COVID-19 in city clusters. The results suggest that human intervention significantly inhibits the high correlation between population mobility and infection cases. Furthermore, by simulating different population flow scenarios, we reveal spatial diffusion phenomenon of cases from cities with high infection density to cities with low infection density. Finally, our model exhibits acceptable prediction performance using actual case data. The localized analytical results verify the ability of the PDE model to correctly describe the epidemic propagation and provide new insights for controlling the spread of COVID-19.

Li Ruqi, Song Yurong, Wang Haiyan, Jiang Guo-Ping, Xiao Min

2022-Nov-21

City clusters, Human mobility networks, Intervention, Reactive-diffusion epidemic model, Threshold behavior

Surgery Surgery

The optimal use of colon capsule endoscopes in clinical practice.

In Therapeutic advances in chronic disease

Colon capsule endoscopy (CCE) has been available for nearly two decades but has grappled with being an equal diagnostic alternative to optical colonoscopy (OC). Due to the COVID-19 pandemic, CCE has gained more foothold in clinical practice. In this cutting-edge review, we aim to present the existing knowledge on the pros and cons of CCE and discuss whether the modality is ready for a larger roll-out in clinical settings. We have included clinical trials and reviews with the most significant impact on the current position of CCE in clinical practice and discuss the challenges that persist and how they could be addressed to make CCE a more sustainable imaging modality with an adenoma detection rate equal to OC and a low re-investigation rate by a proper preselection of suitable populations. CCE is embedded with a very low risk of severe complications and can be performed in the patient's home as a pain-free procedure. The diagnostic accuracy is found to be equal to OC. However, a significant drawback is low completion rates eliciting a high re-investigation rate. Furthermore, the bowel preparation before CCE is extensive due to the high demand for clean mucosa. CCE is currently not suitable for large-scale implementation in clinical practice mainly due to high re-investigation rates. By a better preselection before CCE and the implantation of artificial intelligence for picture and video analysis, CCE could be the alternative to OC needed to move away from in-hospital services and relieve long-waiting lists for OC.

Bjørsum-Meyer Thomas, Koulaouzidis Anastasios, Baatrup Gunnar

2022

artificial intelligence, capsule endoscopy, colonic disease, endoscopy, routine diagnostic test, wireless capsule endoscopy

General General

Food waste reduction and economic savings in times of crisis: The potential of machine learning methods to plan guest attendance in Swedish public catering during the Covid-19 pandemic.

In Socio-economic planning sciences

Food waste is a significant problem within public catering establishments in any normal situation. During spring 2020 the Covid-19 pandemic placed the public catering system under greater pressure, revealing weaknesses within the system and generation of food waste due to rapidly changing consumption patterns. In times of crisis, it is especially important to conserve resources and allocate existing resources to areas where they can be of most use, but this poses significant challenges. This study evaluated the potential of a forecasting model to predict guest attendance during the start and throughout the pandemic. This was done by collecting data on guest attendance in Swedish school and preschool catering establishments before and during the pandemic, and using a machine learning approach to predict future guest attendance based on historical data. Comparison of various learning methods revealed that random forest produced more accurate forecasts than a simple artificial neural network, with conditional mean absolute prediction error of < 0.15 for the trained dataset. Economic savings were obtained by forecasting compared with a no-plan scenario, supporting selection of the random forest approach for effective forecasting of meal planning. Overall, the results obtained using forecasting models for meal planning in times of crisis confirmed their usefulness. Continuous use can improve estimates for the test period, due to the agile and flexible nature of these models. This is particularly important when guest attendance is unpredictable, so that production planning can be optimized to reduce food waste and contribute to a more sustainable and resilient food system.

Malefors Christopher, Secondi Luca, Marchetti Stefano, Eriksson Mattias

2022-Aug

Food waste school kitchens forecasting random-forest system optimization

General General

Natural language processing analysis applied to COVID-19 open-text opinions using a distilBERT model for sentiment categorization.

In AI & society

COVID-19 is a disease that affects the quality of life in all aspects. However, the government policy applied in 2020 impacted the lifestyle of the whole world. In this sense, the study of sentiments of people in different countries is a very important task to face future challenges related to lockdown caused by a virus. To contribute to this objective, we have proposed a natural language processing model with the aim to detect positive and negative feelings in open-text answers obtained from a survey in pandemic times. We have proposed a distilBERT transformer model to carry out this task. We have used three approaches to perform a comparison, obtaining for our best model the following average metrics: Accuracy: 0.823, Precision: 0.826, Recall: 0.793 and F1 Score: 0.803.

Jojoa Mario, Eftekhar Parvin, Nowrouzi-Kia Behdin, Garcia-Zapirain Begonya

2022-Nov-21

Deep learning, DistilBERT, Natural language processing, Sentiment analysis, Transformer

General General

Variational Autoencoder Based Imbalanced COVID-19 Detection Using Chest X-Ray Images.

In New generation computing

Early and fast detection of disease is essential for the fight against COVID-19 pandemic. Researchers have focused on developing robust and cost-effective detection methods using Deep learning based chest X-Ray image processing. However, such prediction models are often not well suited to address the challenge of highly imabalanced datasets. The current work is an attempt to address the issue by utilizing unsupervised Variational Auto Encoders (VAEs). Firstly, chest X-Ray images are converted to a latent space by learning the most important features using VAEs. Secondly, a wide range of well established data resampling techniques are used to balance the preexisting imbalanced classes in the latent vector form of the dataset. Finally, the modified dataset in the new feature space is used to train well known classification models to classify chest X-Ray images into three different classes viz., "COVID-19", "Pneumonia", and "Normal". In order to capture the quality of resampling methods, 10-folds cross validation technique is applied on the dataset. Extensive experimental analysis have been carried out and results so obtained indicate significant improvement in COVID-19 detection using the proposed VAE based method. Furthermore, the ingenuity of the results have been established by performing Wilcoxon rank test with 95% level of significance.

Chatterjee Sankhadeep, Maity Soumyajit, Bhattacharjee Mayukh, Banerjee Soumen, Das Asit Kumar, Ding Weiping

2022-Nov-19

COVID-19, Class imbalance, Oversampling, Undersampling, Variational autoencoder

General General

Combined Cloud-Based Inference System for the Classification of COVID-19 in CT-Scan and X-Ray Images.

In New generation computing

In the past few years, most of the work has been done around the classification of covid-19 using different images like CT-scan, X-ray, and ultrasound. But none of that is capable enough to deal with each of these image types on a single common platform and can identify the possibility that a person is suffering from COVID or not. Thus, we realized there should be a platform to identify COVID-19 in CT-scan and X-ray images on the fly. So, to fulfill this need, we proposed an AI model to identify CT-scan and X-ray images from each other and then use this inference to classify them of COVID positive or negative. The proposed model uses the inception architecture under the hood and trains on the open-source extended covid-19 dataset. The dataset consists of plenty of images for both image types and is of size 4 GB. We achieved an accuracy of 100%, average macro-Precision of 100%, average macro-Recall of 100%, average macro f1-score of 100%, and AUC score of 99.6%. Furthermore, in this work, cloud-based architecture is proposed to massively scale and load balance as the Number of user requests rises. As a result, it will deliver a service with minimal latency to all users.

Dubey Ankit Kumar, Mohbey Krishna Kumar

2022-Nov-20

Area under curve, Artificial intelligence, COVID 19, Computed tomography, Inception, Transfer learning

General General

Artificial intelligence-based internet hospital pharmacy services in China: Perspective based on a case study.

In Frontiers in pharmacology

Background: Recently, internet hospitals have been emerging in China, saving patients time and money during the COVID-19 pandemic. In addition, pharmacy services that link doctors and patients are becoming essential in improving patient satisfaction. However, the existing internet hospital pharmacy service mode relies primarily on manual operations, making it cumbersome, inefficient, and high-risk. Objective: To establish an internet hospital pharmacy service mode based on artificial intelligence (AI) and provide new insights into pharmacy services in internet hospitals during the COVID-19 pandemic. Methods: An AI-based internet hospital pharmacy service mode was established. Initially, prescription rules were formulated and embedded into the internet hospital system to review the prescriptions using AI. Then, the "medicine pick-up code," which is a Quick Response (QR) code that represents a specific offline self-pick-up order, was created. Patients or volunteers could pick up medications at an offline hospital or drugstore by scanning the QR code through the window and wait for the dispensing machine or pharmacist to dispense the drugs. Moreover, the medication consultation function was also operational. Results: The established internet pharmacy service mode had four major functional segments: online drug catalog search, prescription preview by AI, drug dispensing and distribution, and AI-based medication consultation response. The qualified rate of AI preview was 83.65%. Among the 16.35% inappropriate prescriptions, 49% were accepted and modified by physicians proactively and 51.00% were passed after pharmacists intervened. The "offline self-pick-up" mode was preferred by 86% of the patients for collecting their medication in the internet hospital, which made the QR code to be fully applied. A total of 426 medication consultants were served, and 48.83% of them consulted outside working hours. The most frequently asked questions during consultations were about the internet hospital dispensing process, followed by disease diagnosis, and patient education. Therefore, an AI-based medication consultation was proposed to respond immediately when pharmacists were unavailable. Conclusion: The established AI-based internet hospital pharmacy service mode could provide references for pharmacy departments during the COVID-19 pandemic. The significance of this study lies in ensuring safe/rational use of medicines and raising pharmacists' working efficiency.

Bu Fengjiao, Sun Hong, Li Ling, Tang Fengmin, Zhang Xiuwen, Yan Jingchao, Ye Zhengqiang, Huang Taomin

2022

artificial intelligence, internet hospital, medication pick-up code, online medication consultation, prescription preview

General General

Space-Distributed Traffic-Enhanced LSTM-Based Machine Learning Model for COVID-19 Incidence Forecasting.

In Computational intelligence and neuroscience

The COVID-19 virus continues to generate waves of infections around the world. With major areas in developing countries still lagging behind in vaccination campaigns, the risk of new variants that can cause re-infections worldwide makes the monitoring and forecasting of the evolution of the virus a high priority. Having accurate models able to forecast the incidence of the spread of the virus provides help to policymakers and health professionals in managing the scarce resources in an optimal way. In this paper, a new machine learning model is proposed to forecast the spread of the virus one-week ahead in a geographic area which combines mobility and COVID-19 incidence data. The area is divided into zones or districts according to the location of the COVID-19 measuring points. A traffic-driven mobility estimate among adjacent districts is proposed to capture the spatial spread of the virus. Traffic-driven mobility in adjacent districts will be used together with COVID-19 incidence data to feed a new deep learning LSTM-based model which will extract patterns from mobility-modulated COVID-19 incidence spatiotemporal data in order to optimize one-week ahead estimations. The model is trained and validated with open data available for the city of Madrid (Spain) for 3 different validation scenarios. A baseline model based on previous literature able to extract temporal patterns in COVID-19 incidence time series is also trained with the same dataset. The results show that the proposed model, based on the combination of traffic and COVID-19 incidence data, is able to outperform the baseline model in all the validation scenarios.

Muñoz-Organero Mario

2022

General General

An ensemble prediction model for COVID-19 mortality risk.

In Biology methods & protocols

BACKGROUND : It's critical to identify COVID-19 patients with a higher death risk at early stage to give them better hospitalization or intensive care. However, thus far, none of the machine learning models has been shown to be successful in an independent cohort. We aim to develop a machine learning model which could accurately predict death risk of COVID-19 patients at an early stage in other independent cohorts.

METHODS : We used a cohort containing 4711 patients whose clinical features associated with patient physiological conditions or lab test data associated with inflammation, hepatorenal function, cardiovascular function, and so on to identify key features. To do so, we first developed a novel data preprocessing approach to clean up clinical features and then developed an ensemble machine learning method to identify key features.

RESULTS : Finally, we identified 14 key clinical features whose combination reached a good predictive performance of area under the receiver operating characteristic curve 0.907. Most importantly, we successfully validated these key features in a large independent cohort containing 15 790 patients.

CONCLUSIONS : Our study shows that 14 key features are robust and useful in predicting the risk of death in patients confirmed SARS-CoV-2 infection at an early stage, and potentially useful in clinical settings to help in making clinical decisions.

Li Jie, Li Xin, Hutchinson John, Asad Mohammad, Liu Yinghui, Wang Yadong, Wang Edwin

2022

COVID-19, SARS-CoV-2, cohort studies, mortality prediction, prognosis

General General

A semi-supervised Bayesian mixture modelling approach for joint batch correction and classification

bioRxiv Preprint

Systematic differences between batches of samples present significant challenges when analysing biological data. Such batch effects are well-studied and are liable to occur in any setting where multiple batches are assayed. Many existing methods for accounting for these have focused on high-dimensional data such as RNA-seq and have assumptions that reflect this. Here we focus on batch-correction in low-dimensional classification problems. We propose a semi-supervised Bayesian generative classifier based on mixture models that jointly predicts class labels and models batch effects. Our model allows observations to be probabilistically assigned to classes in a way that incorporates uncertainty arising from batch effects. By simultaneously inferring the classification and the batch-correction our method is more robust to dependence between batch and class than pre-processing steps such as ComBat. We explore two choices for the within-class densities: the multivariate normal and the multivariate t. A simulation study demonstrates that our method performs well compared to popular off-the-shelf machine learning methods and is also quick; performing 15,000 iterations on a dataset of 750 samples with 2 measurements each in 11.7 seconds for the MVN mixture model and 14.7 seconds for the MVT mixture model. We further validate our model on gene expression data where cell type (class) is known and simulate batch effects. We apply our model to two datasets generated using the enzyme-linked immunosorbent assay (ELISA), a spectrophotometric assay often used to screen for antibodies. The examples we consider were collected in 2020 and measure seropositivity for SARS-CoV-2. We use our model to estimate seroprevalence in the populations studied. We implement the models in C++ using a Metropolis-within-Gibbs algorithm, available in the R package batchmix. Scripts to recreate our analysis are at https://github.com/stcolema/BatchClassifierPaper.

Coleman, S.; Nicholls, K. C.; Castro Dopico, X.; Karlsson Hedestam, G. B.; Kirk, P. D.; Wallace, C.

2022-11-29

Public Health Public Health

Machine learning based regional epidemic transmission risks precaution in digital society.

In Scientific reports ; h5-index 158.0

The contact and interaction of human is considered to be one of the important factors affecting the epidemic transmission, and it is critical to model the heterogeneity of individual activities in epidemiological risk assessment. In digital society, massive data makes it possible to implement this idea on large scale. Here, we use the mobile phone signaling to track the users' trajectories and construct contact network to describe the topology of daily contact between individuals dynamically. We show the spatiotemporal contact features of about 7.5 million mobile phone users during the outbreak of COVID-19 in Shanghai, China. Furthermore, the individual feature matrix extracted from contact network enables us to carry out the extreme event learning and predict the regional transmission risk, which can be further decomposed into the risk due to the inflow of people from epidemic hot zones and the risk due to people close contacts within the observing area. This method is much more flexible and adaptive, and can be taken as one of the epidemic precautions before the large-scale outbreak with high efficiency and low cost.

Shi Zhengyu, Qian Haoqi, Li Yao, Wu Fan, Wu Libo

2022-Nov-28

Public Health Public Health

Contextual factors predicting compliance behavior during the COVID-19 pandemic: A machine learning analysis on survey data from 16 countries.

In PloS one ; h5-index 176.0

Voluntary isolation is one of the most effective methods for individuals to help prevent the transmission of diseases such as COVID-19. Understanding why people leave their homes when advised not to do so and identifying what contextual factors predict this non-compliant behavior is essential for policymakers and public health officials. To provide insight on these factors, we collected data from 42,169 individuals across 16 countries. Participants responded to items inquiring about their socio-cultural environment, such as the adherence of fellow citizens, as well as their mental states, such as their level of loneliness and boredom. We trained random forest models to predict whether someone had left their home during a one week period during which they were asked to voluntarily isolate themselves. The analyses indicated that overall, an increase in the feeling of being caged leads to an increased probability of leaving home. In addition, an increased feeling of responsibility and an increased fear of getting infected decreased the probability of leaving home. The models predicted compliance behavior with between 54% and 91% accuracy within each country's sample. In addition, we modeled factors leading to risky behavior in the pandemic context. We observed an increased probability of visiting risky places as both the anticipated number of people and the importance of the activity increased. Conversely, the probability of visiting risky places increased as the perceived putative effectiveness of social distancing decreased. The variance explained in our models predicting risk ranged from < .01 to .54 by country. Together, our findings can inform behavioral interventions to increase adherence to lockdown recommendations in pandemic conditions.

Hajdu Nandor, Schmidt Kathleen, Acs Gergely, Röer Jan P, Mirisola Alberto, Giammusso Isabella, Arriaga Patrícia, Ribeiro Rafael, Dubrov Dmitrii, Grigoryev Dmitry, Arinze Nwadiogo C, Voracek Martin, Stieger Stefan, Adamkovic Matus, Elsherif Mahmoud, Kern Bettina M J, Barzykowski Krystian, Ilczuk Ewa, Martončik Marcel, Ropovik Ivan, Ruiz-Fernandez Susana, Baník Gabriel, Ulloa José Luis, Aczel Balazs, Szaszi Barnabas

2022

Cardiology Cardiology

Advances in Cardiac Electrophysiology.

In Circulation. Arrhythmia and electrophysiology

Despite the global COVID-19 pandemic, during the past 2 years, there have been numerous advances in our understanding of arrhythmia mechanisms and diagnosis and in new therapies. We increased our understanding of risk factors and mechanisms of atrial arrhythmias, the prediction of atrial arrhythmias, response to treatment, and outcomes using machine learning and artificial intelligence. There have been new technologies and techniques for atrial fibrillation ablation, including pulsed field ablation. There have been new randomized trials in atrial fibrillation ablation, giving insight about rhythm control, and long-term outcomes. There have been advances in our understanding of treatment of inherited disorders such as catecholaminergic polymorphic ventricular tachycardia. We have gained new insights into the recurrence of ventricular arrhythmias in the setting of various conditions such as myocarditis and inherited cardiomyopathic disorders. Novel computational approaches may help predict occurrence of ventricular arrhythmias and localize arrhythmias to guide ablation. There are further advances in our understanding of noninvasive radiotherapy. We have increased our understanding of the role of His bundle pacing and left bundle branch area pacing to maintain synchronous ventricular activation. There have also been significant advances in the defibrillators, cardiac resynchronization therapy, remote monitoring, and infection prevention. There have been advances in our understanding of the pathways and mechanisms involved in atrial and ventricular arrhythmogenesis.

Piccini Jonathan P, Russo Andrea M, Sharma Parikshit S, Kron Jordana, Tzou Wendy, Sauer William, Park David S, Birgersdotter-Green Ulrika, Frankel David S, Healey Jeff S, Hummel John, Koruth Jacob, Linz Dominik, Mittal Suneet, Nair Devi G, Nattel Stanley, Noseworthy Peter A, Steinberg Benjamin A, Trayanova Natalia A, Wan Elaine Y, Wissner Erik, Zeitler Emily P, Wang Paul J

2022-Nov-28

arrhythmias, atrial fibrillation, cardiac electrophysiology, implantable defibrillators, ventricular tachycardia

Cardiology Cardiology

Comparison of machine learning methods with logistic regression analysis in creating predictive models for risk of critical in-hospital events in COVID-19 patients on hospital admission.

In BMC medical informatics and decision making ; h5-index 38.0

BACKGROUND : Machine learning (ML) algorithms have been trained to early predict critical in-hospital events from COVID-19 using patient data at admission, but little is known on how their performance compares with each other and/or with statistical logistic regression (LR). This prospective multicentre cohort study compares the performance of a LR and five ML models on the contribution of influencing predictors and predictor-to-event relationships on prediction model´s performance.

METHODS : We used 25 baseline variables of 490 COVID-19 patients admitted to 8 hospitals in Germany (March-November 2020) to develop and validate (75/25 random-split) 3 linear (L1 and L2 penalty, elastic net [EN]) and 2 non-linear (support vector machine [SVM] with radial kernel, random forest [RF]) ML approaches for predicting critical events defined by intensive care unit transfer, invasive ventilation and/or death (composite end-point: 181 patients). Models were compared for performance (area-under-the-receiver-operating characteristic-curve [AUC], Brier score) and predictor importance (performance-loss metrics, partial-dependence profiles).

RESULTS : Models performed close with a small benefit for LR (utilizing restricted cubic splines for non-linearity) and RF (AUC means: 0.763-0.731 [RF-L1]); Brier scores: 0.184-0.197 [LR-L1]). Top ranked predictor variables (consistently highest importance: C-reactive protein) were largely identical across models, except creatinine, which exhibited marginal (L1, L2, EN, SVM) or high/non-linear effects (LR, RF) on events.

CONCLUSIONS : Although the LR and ML models analysed showed no strong differences in performance and the most influencing predictors for COVID-19-related event prediction, our results indicate a predictive benefit from taking account for non-linear predictor-to-event relationships and effects. Future efforts should focus on leveraging data-driven ML technologies from static towards dynamic modelling solutions that continuously learn and adapt to changes in data environments during the evolving pandemic.

TRIAL REGISTRATION NUMBER : NCT04659187.

Sievering Aaron W, Wohlmuth Peter, Geßler Nele, Gunawardene Melanie A, Herrlinger Klaus, Bein Berthold, Arnold Dirk, Bergmann Martin, Nowak Lorenz, Gloeckner Christian, Koch Ina, Bachmann Martin, Herborn Christoph U, Stang Axel

2022-Nov-28

COVID-19, Clinical decision-making, Critical event prediction, Machine learning, Predictive models

Public Health Public Health

Applied artificial intelligence in healthcare: Listening to the winds of change in a post-COVID-19 world.

In Experimental biology and medicine (Maywood, N.J.)

This editorial article aims to highlight advances in artificial intelligence (AI) technologies in five areas: Collaborative AI, Multimodal AI, Human-Centered AI, Equitable AI, and Ethical and Value-based AI in order to cope with future complex socioeconomic and public health issues.

Shaban-Nejad Arash, Michalowski Martin, Bianco Simone, Brownstein John S, Buckeridge David L, Davis Robert L

2022-Nov-25

AI governance, COVID-19, Health AI, artificial intelligence, ethical AI, human-centered AI, machine learning, multimodal AI

General General

Epidemiology and clinical features of SARS-CoV-2 infection in hospitalized children across four waves in Hungary: A retrospective, comparative study from March 2020 to December 2021.

In Health science reports

BACKGROUND AND AIMS : From 2019 till the present, infections induced by the novel coronavirus and its mutations have posed a new challenge for healthcare. However, comparative studies on pediatric infections throughout waves are few. During four different pandemic waves, we intended to investigate the clinical and epidemiological characteristic of the pediatric population hospitalized for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus infection.

METHODS : Between March 2020 and December 2021, we performed our retrospective research on children infected with the SARS-CoV-2 virus at the University of Szeged. We analyzed the data of all patients who required hospitalization due to positive results of SARS-CoV-2 tests (Nucleic Acid Amplification Test or rapid antigen test). Data analysis included demographic data, medical history, clinical findings, length of hospitalization, and complications, using medical records.

RESULTS : In this study, data from 358 coronavirus-infected children were analyzed. The most affected age group was children over 1 month and under 1 year (30.2%). The highest number of cases was recorded in the fourth wave (53.6%). Fever (65.6%), cough (51.4%), nasal discharge (35.3%), nausea and vomiting (31.3%), and decreased oral intake (28.9%) were the most common symptoms. The most common complications were dehydration (50.5%), pneumonia (14.9%), and bronchitis/bronchiolitis (14.5%). Based on RR values, there are considerable differences in the prevalence of the symptoms and complications between the different age groups and waves. Cox proportional hazard model analyzes showed that fever and tachypnoea had a relevant effect on days to recovery.

CONCLUSIONS : We found trends similar to those previously published, overall statistics. The proportion of children requiring hospitalization varied from wave to wave, with the fourth wave affecting the Hungarian child population the most. Our findings suggest that hospitalization time is unrelated to age, but that certain symptoms (fever and tachypnoea) are associated with longer hospitalization. The onset of certain symptoms may differ by age group.

Takács Andrea T, Bukva Mátyás, Gavallér Gabriella, Kapus Katalin, Rózsa Mária, Bán-Gagyi Boglárka, Sinkó Mária, Szűcs Dániel, Terhes Gabriella, Bereczki Csaba

2022-Nov

COVID‐19, SARS‐CoV‐2 infection, children, hospitalization wave

Public Health Public Health

Prediction of Global Psychological Stress and Coping Induced by the COVID-19 Outbreak: A Machine Learning Study.

In Alpha psychiatry

BACKGROUND : Artificial intelligence and machine learning have enormous potential to deal efficiently with a wide range of issues that traditional sciences may be unable to address. Neuroscience, particularly psychiatry, is one of the domains that could potentially benefit from artificial intelligence and machine learning. This study aims to predict Stress and assess Coping with stress mechanisms during the COVID-19 pandemic and, therefore, help establish a successful intervention to manage distress.

METHODS : COVIDiSTRESS global survey data was used in this study and comprised 70 652 respondents after pre-processing. Binary classification is performed for predicting Stress and Coping with stress, while 2 ensemble machine learning algorithms, deep super learner and cascade deep forest, and state-of-the-art methods are explored for classification. Correlation attribute evaluation is used for feature significance. Statistical analysis, such as Cronbach's alpha, demographic statistics, Pearson's correlation coefficient, independent sample t-test, and 95% CI, is also performed.

RESULTS : Globally, females, the younger population, and those in COVID-19 risk groups are observed to possess higher levels of stress. Trust, Loneliness, and Distress are found to be the primary predictors of Stress, whereas the significant predictors for coping with stress are identified as Social Provision, Extroversion, and Agreeableness. Deep super learner and cascade deep forest outperformed the state-of-the-art methods with an accuracy of up to 88.42%.

CONCLUSIONS : By comparing different classifiers, we can conclude that multi-layer ensemble outperforms all. Another aim of this study, is the ability to regulate demographic and negative psychological states with a goal of medical interventions and to work towards building multiple coping strategies to reduce stress and promote resilience and recovery from COVID-19.

Prerna Tigga Neha, Garg Shruti

2022-Jul

COVID-19, coping, machine learning, public health, stress

General General

COVID-19 screening with digital holographic microscopy using intra-patient probability functions of spatio-temporal bio-optical attributes.

In Biomedical optics express

We present an automated method for COVID-19 screening using the intra-patient population distributions of bio-optical attributes extracted from digital holographic microscopy reconstructed red blood cells. Whereas previous approaches have aimed to identify infection by classifying individual cells, here, we propose an approach to incorporate the attribute distribution information from the population of a given human subjects' cells into our classification scheme and directly classify subjects at the patient level. To capture the intra-patient distribution information in a generalized way, we propose an approach based on the Bag-of-Features (BoF) methodology to transform histograms of bio-optical attribute distributions into feature vectors for classification via a linear support vector machine. We compare our approach with simpler classifiers directly using summary statistics such as mean, standard deviation, skewness, and kurtosis of the distributions. We also compare to a k-nearest neighbor classifier using the Kolmogorov-Smirnov distance as a distance metric between the attribute distributions of each subject. We lastly compare our approach to previously published methods for classification of individual red blood cells. In each case, the methodology proposed in this paper provides the highest patient classification performance, correctly classifying 22 out of 24 individuals and achieving 91.67% classification accuracy with 90.00% sensitivity and 92.86% specificity. The incorporation of distribution information for classification additionally led to the identification of a singular temporal-based bio-optical attribute capable of highly accurate patient classification. To the best of our knowledge, this is the first report of a machine learning approach using the intra-patient probability distribution information of bio-optical attributes obtained from digital holographic microscopy for disease screening.

O’Connor Timothy, Javidi Bahram

2022-Oct-01

General General

Dual_Pachi: Attention-based dual path framework with intermediate second order-pooling for Covid-19 detection from chest X-ray images.

In Computers in biology and medicine

Numerous machine learning and image processing algorithms, most recently deep learning, allow the recognition and classification of COVID-19 disease in medical images. However, feature extraction, or the semantic gap between low-level visual information collected by imaging modalities and high-level semantics, is the fundamental shortcoming of these techniques. On the other hand, several techniques focused on the first-order feature extraction of the chest X-Ray thus making the employed models less accurate and robust. This study presents Dual_Pachi: Attention Based Dual Path Framework with Intermediate Second Order-Pooling for more accurate and robust Chest X-ray feature extraction for Covid-19 detection. Dual_Pachi consists of 4 main building Blocks; Block one converts the received chest X-Ray image to CIE LAB coordinates (L & AB channels which are separated at the first three layers of a modified Inception V3 Architecture.). Block two further exploit the global features extracted from block one via a global second-order pooling while block three focuses on the low-level visual information and the high-level semantics of Chest X-ray image features using a multi-head self-attention and an MLP Layer without sacrificing performance. Finally, the fourth block is the classification block where classification is done using fully connected layers and SoftMax activation. Dual_Pachi is designed and trained in an end-to-end manner. According to the results, Dual_Pachi outperforms traditional deep learning models and other state-of-the-art approaches described in the literature with an accuracy of 0.96656 (Data_A) and 0.97867 (Data_B) for the Dual_Pachi approach and an accuracy of 0.95987 (Data_A) and 0.968 (Data_B) for the Dual_Pachi without attention block model. A Grad-CAM-based visualization is also built to highlight where the applied attention mechanism is concentrated.

Ukwuoma Chiagoziem C, Qin Zhiguang, Agbesi Victor K, Cobbinah Bernard M, Yussif Sophyani B, Abubakar Hassan S, Lemessa Bona D

2022-Nov-18

Attention mechanism, COVID-19 detection, Chest X-rays images, Deep learning, Feature extraction, Global second-order pooling

Public Health Public Health

The Role of Natural Language Processing during the COVID-19 Pandemic: Health Applications, Opportunities, and Challenges.

In Healthcare (Basel, Switzerland)

The COVID-19 pandemic is the most devastating public health crisis in at least a century and has affected the lives of billions of people worldwide in unprecedented ways. Compared to pandemics of this scale in the past, societies are now equipped with advanced technologies that can mitigate the impacts of pandemics if utilized appropriately. However, opportunities are currently not fully utilized, particularly at the intersection of data science and health. Health-related big data and technological advances have the potential to significantly aid the fight against such pandemics, including the current pandemic's ongoing and long-term impacts. Specifically, the field of natural language processing (NLP) has enormous potential at a time when vast amounts of text-based data are continuously generated from a multitude of sources, such as health/hospital systems, published medical literature, and social media. Effectively mitigating the impacts of the pandemic requires tackling challenges associated with the application and deployment of NLP systems. In this paper, we review the applications of NLP to address diverse aspects of the COVID-19 pandemic. We outline key NLP-related advances on a chosen set of topics reported in the literature and discuss the opportunities and challenges associated with applying NLP during the current pandemic and future ones. These opportunities and challenges can guide future research aimed at improving the current health and social response systems and pandemic preparedness.

Al-Garadi Mohammed Ali, Yang Yuan-Chi, Sarker Abeed

2022-Nov-12

COVID-19, deep learning, health applications, machine learning, natural language processing

General General

Automated Lung-Related Pneumonia and COVID-19 Detection Based on Novel Feature Extraction Framework and Vision Transformer Approaches Using Chest X-ray Images.

In Bioengineering (Basel, Switzerland)

According to research, classifiers and detectors are less accurate when images are blurry, have low contrast, or have other flaws which raise questions about the machine learning model's ability to recognize items effectively. The chest X-ray image has proven to be the preferred image modality for medical imaging as it contains more information about a patient. Its interpretation is quite difficult, nevertheless. The goal of this research is to construct a reliable deep-learning model capable of producing high classification accuracy on chest x-ray images for lung diseases. To enable a thorough study of the chest X-ray image, the suggested framework first derived richer features using an ensemble technique, then a global second-order pooling is applied to further derive higher global features of the images. Furthermore, the images are then separated into patches and position embedding before analyzing the patches individually via a vision transformer approach. The proposed model yielded 96.01% sensitivity, 96.20% precision, and 98.00% accuracy for the COVID-19 Radiography Dataset while achieving 97.84% accuracy, 96.76% sensitivity and 96.80% precision, for the Covid-ChestX-ray-15k dataset. The experimental findings reveal that the presented models outperform traditional deep learning models and other state-of-the-art approaches provided in the literature.

Ukwuoma Chiagoziem C, Qin Zhiguang, Heyat Md Belal Bin, Akhtar Faijan, Smahi Abla, Jackson Jehoiada K, Furqan Qadri Syed, Muaad Abdullah Y, Monday Happy N, Nneji Grace U

2022-Nov-18

COVID-19, artificial intelligence, automatic detection, chest X-rays images, epidemic, feature extraction, lung disease, pneumonia

General General

Diverse COVID-19 CT Image-to-Image Translation with Stacked Residual Dropout.

In Bioengineering (Basel, Switzerland)

Machine learning models are renowned for their high dependency on a large corpus of data in solving real-world problems, including the recent COVID-19 pandemic. In practice, data acquisition is an onerous process, especially in medical applications, due to lack of data availability for newly emerged diseases and privacy concerns. This study introduces a data synthesization framework (sRD-GAN) that generates synthetic COVID-19 CT images using a novel stacked-residual dropout mechanism (sRD). sRD-GAN aims to alleviate the problem of data paucity by generating synthetic lung medical images that contain precise radiographic annotations. The sRD mechanism is designed using a regularization-based strategy to facilitate perceptually significant instance-level diversity without content-style attribute disentanglement. Extensive experiments show that sRD-GAN can generate exceptional perceptual realism on COVID-19 CT images examined by an experiment radiologist, with an outstanding Fréchet Inception Distance (FID) of 58.68 and Learned Perceptual Image Patch Similarity (LPIPS) of 0.1370 on the test set. In a benchmarking experiment, sRD-GAN shows superior performance compared to GAN, CycleGAN, and one-to-one CycleGAN. The encouraging results achieved by sRD-GAN in different clinical cases, such as community-acquired pneumonia CT images and COVID-19 in X-ray images, suggest that the proposed method can be easily extended to other similar image synthetization problems.

Lee Kin Wai, Chin Renee Ka Yin

2022-Nov-16

COVID-19, chest computed tomography, generative adversarial networks, image synthesis

General General

Portable, Automated and Deep-Learning-Enabled Microscopy for Smartphone-Tethered Optical Platform Towards Remote Homecare Diagnostics: A Review.

In Small methods

Globally new pandemic diseases induce urgent demands for portable diagnostic systems to prevent and control infectious diseases. Smartphone-based portable diagnostic devices are significantly efficient tools to user-friendly connect personalized health conditions and collect valuable optical information for rapid diagnosis and biomedical research through at-home screening. Deep learning algorithms for portable microscopes also help to enhance diagnostic accuracy by reducing the imaging resolution gap between benchtop and portable microscopes. This review highlighted recent progress and continued efforts in a smartphone-tethered optical platform through portable, automated, and deep-learning-enabled microscopy for personalized diagnostics and remote monitoring. In detail, the optical platforms through smartphone-based microscopes and lens-free holographic microscopy are introduced, and deep learning-based portable microscopic imaging is explained to improve the image resolution and accuracy of diagnostics. The challenges and prospects of portable optical systems with microfluidic channels and a compact microscope to screen COVID-19 in the current pandemic are also discussed. It has been believed that this review offers a novel guide for rapid diagnosis, biomedical imaging, and digital healthcare with low cost and portability.

Kim Kisoo, Lee Won Gu

2022-Nov-24

Smartphone-Tethered Optical Platform, deep learning-enhanced microscopic imaging, lens-free holographic imaging, personalized diagnostics, portable COVID screening system, smartphone-based microscopy

General General

A systematic review: Chest radiography images (X-ray images) analysis and COVID-19 categorization diagnosis using artificial intelligence techniques.

In Network (Bristol, England)

COVID-19 pandemic created a turmoil across nations due to Severe Acute Respiratory Syndrome Corona virus-1(SARS - Co-V-2). The severity of COVID-19 symptoms is starting from cold, breathing problems, issues in respiratory system which may also lead to life threatening situations. This disease is widely contaminating and transmitted from man-to-man. The contamination is spreading when the human organs like eyes, nose, and mouth get in contact with contaminated fluids. This virus can be screened through performing a nasopharyngeal swab test which is time consuming. So the physicians are preferring the fast detection methods like chest radiography images and CT scans. At times some confusion in finding out the accurate disorder from chest radiography images can happen. To overcome this issue this study reviews several deep learning and machine learning procedures to be implemented in X-ray images of chest. This also helps the professionals to find out the other types of malfunctions happening in the chest other than COVID-19 also. This review can act as a guidance to the doctors and radiologists in identifying the COVID-19 and other types of viruses causing illness in the human anatomy and can provide aid soon.

Suba Saravanan, Muthulakshmi M

2022-Nov-24

AI tools deep learning approaches, COVID-19, chest radiography images, machine learning approaches

Public Health Public Health

COVID-19 smart surveillance: Examination of Knowledge of Apps and mobile thermometer detectors (MTDs) in a high-risk society.

In Digital health

BACKGROUND : Technological innovations gained momentum and supported COVID-19 intelligence surveillance among high-risk populations globally. We examined technology surveillance using mobile thermometer detectors (MTDs), knowledge of App, and self-efficacy as a means of sensing body temperature as a measure of COVID-19 risk mitigation. In a cross-sectional survey, we explored COVID-19 risk mitigation, mobile temperature detectable by network syndromic surveillance mobility, detachable from clinicians, and laboratory diagnoses to elucidate the magnitude of community monitoring.

MATERIALS AND METHODS : In a cross-sectional survey, we create in-depth comprehension of risk mitigation, mobile temperature Thermometer detector, and other variables for surveillance and monitoring among 850 university students and healthcare workers. An applied structural equation model was adopted for analysis with Amos v.24. We established that mobile usability knowledge of APP could effectively aid in COVID-19 intelligence risk mitigation. Moreover, both self-efficacy and mobile temperature positively strengthened data visualization for public health decision-making.

RESULTS : The algorithms utilize a validated point-of-center test to ascertain the HealthCode scanning system for a positive or negative COVID-19 notification. The MTD is an alternative personal self-testing procedure used to verify temperature rates based on previous SARS-CoV-2 and future mobility digital health. Personal self-care of MTD mobility and knowledge of mHealth apps can specifically manage COVID-19 mitigation in high or low terrestrial areas. We found mobile usability, mobile self-efficacy, and app knowledge were statistically significant to COVID-19 mitigation. Additionally, interaction strengthened the positive relationship between self-efficacy and COVID-19. Data aggregation is entrusted with government database agencies, using natural language processing and machine learning mechanisms to validate and analyze.

CONCLUSION : The study shows that temperature thermometer detectors, mobile usability, and knowledge of App enhanced COVID-19 risk mitigation in a high or low-risk environment. The standardizing dataset is necessary to ensure privacy and security preservation of data ethics.

Sayibu Muhideen, Chu Jianxun, Tosin Yinka Akintunde, Rufai Olayemi Hafeez, Shahani Riffat, Jin M A

2022

COVID-19 surveillance, knowledge of app, mobile intelligence, mobile thermometer detectors (MTD), risk mitigation

Public Health Public Health

Deep-Data-Driven Neural Networks for COVID-19 Vaccine Efficacy.

In Epidemiolgia (Basel, Switzerland)

Vaccination strategies to lessen the impact of the spread of a disease are fundamental to public health authorities and policy makers. The socio-economic benefit of full return to normalcy is the core of such strategies. In this paper, a COVID-19 vaccination model with efficacy rate is developed and analyzed. The epidemiological parameters of the model are learned via a feed-forward neural network. A hybrid approach that combines residual neural network with variants of recurrent neural network is implemented and analyzed for reliable and accurate prediction of daily cases. The error metrics and a k-fold cross validation with random splitting reveal that a particular type of hybrid approach called residual neural network with gated recurrent unit is the best hybrid neural network architecture. The data-driven simulations confirm the fact that the vaccination rate with higher efficacy lowers the infectiousness and basic reproduction number. As a study case, COVID-19 data for the state of Tennessee in USA is used.

Torku Thomas K, Khaliq Abdul Q M, Furati Khaled M

2021-Nov-30

COVID-19, RNN, ResNet, data-driven, deep learning, k-fold cross validation, vaccination strategy

General General

Variation in the ACE2 receptor has limited utility for SARS-CoV-2 host prediction.

In eLife

Transmission of SARS-CoV-2 from humans to other species threatens wildlife conservation and may create novel sources of viral diversity for future zoonotic transmission. A variety of computational heuristics have been developed to pre-emptively identify susceptible host species based on variation in the angiotensin-converting enzyme 2 (ACE2) receptor used for viral entry. However, the predictive performance of these heuristics remains unknown. Using a newly compiled database of 96 species, we show that, while variation in ACE2 can be used by machine learning models to accurately predict animal susceptibility to sarbecoviruses (accuracy = 80.2%, binomial confidence interval [CI]: 70.8-87.6%), the sites informing predictions have no known involvement in virus binding and instead recapitulate host phylogeny. Models trained on host phylogeny alone performed equally well (accuracy = 84.4%, CI: 75.5-91.0%) and at a level equivalent to retrospective assessments of accuracy for previously published models. These results suggest that the predictive power of ACE2-based models derives from strong correlations with host phylogeny rather than processes which can be mechanistically linked to infection biology. Further, biased availability of ACE2 sequences misleads projections of the number and geographic distribution of at-risk species. Models based on host phylogeny reduce this bias, but identify a very large number of susceptible species, implying that model predictions must be combined with local knowledge of exposure risk to practically guide surveillance. Identifying barriers to viral infection or onward transmission beyond receptor binding and incorporating data which are independent of host phylogeny will be necessary to manage the ongoing risk of establishment of novel animal reservoirs of SARS-CoV-2.

Mollentze Nardus, Keen Deborah, Munkhbayar Uuriintuya, Biek Roman, Streicker Daniel G

2022-Nov-23

ACE2, SARS-CoV-2, ecology, host range, infectious disease, microbiology, viruses

General General

Cov-TransNet: Dual branch fusion network with transformer for COVID-19 infection segmentation.

In Biomedical signal processing and control

Segmentation of COVID-19 infection is a challenging task due to the blurred boundaries and low contrast between the infected and the non-infected areas in COVID-19 CT images, especially for small infection regions. COV-TransNet is presented to achieve high-precision segmentation of COVID-19 infection regions in this paper. The proposed segmentation network is composed of the auxiliary branch and the backbone branch. The auxiliary branch network adopts transformer to provide global information, helping the convolution layers in backbone branch to learn specific local features better. A multi-scale feature attention module is introduced to capture contextual information and adaptively enhance feature representations. Specially, a high internal resolution is maintained during the attention calculation process. Moreover, feature activation module can effectively reduce the loss of valid information during sampling. The proposed network can take full advantage of different depth and multi-scale features to achieve high sensitivity for identifying lesions of varied sizes and locations. We experiment on several datasets of the COVID-19 lesion segmentation task, including COVID-19-CT-Seg, UESTC-COVID-19, MosMedData and COVID-19-MedSeg. Comprehensive results demonstrate that COV-TransNet outperforms the existing state-of-the-art segmentation methods and achieves better segmentation performance for multi-scale lesions.

Peng Yanjun, Zhang Tong, Guo Yanfei

2023-Feb

Attention mechanism, COVID-19, CT images, Deep learning, Semantic segmentation, Transformer

Ophthalmology Ophthalmology

Ischemic stroke of unclear aetiology: a case-by-case analysis and call for a multi-professional predictive, preventive and personalised approach.

In The EPMA journal

Due to the reactive medical approach applied to disease management, stroke has reached an epidemic scale worldwide. In 2019, the global stroke prevalence was 101.5 million people, wherefrom 77.2 million (about 76%) suffered from ischemic stroke; 20.7 and 8.4 million suffered from intracerebral and subarachnoid haemorrhage, respectively. Globally in the year 2019 - 3.3, 2.9 and 0.4 million individuals died of ischemic stroke, intracerebral and subarachnoid haemorrhage, respectively. During the last three decades, the absolute number of cases increased substantially. The current prevalence of stroke is 110 million patients worldwide with more than 60% below the age of 70 years. Prognoses by the World Stroke Organisation are pessimistic: globally, it is predicted that 1 in 4 adults over the age of 25 will suffer stroke in their lifetime. Although age is the best known contributing factor, over 16% of all strokes occur in teenagers and young adults aged 15-49 years and the incidence trend in this population is increasing. The corresponding socio-economic burden of stroke, which is the leading cause of disability, is enormous. Global costs of stroke are estimated at 721 billion US dollars, which is 0.66% of the global GDP. Clinically manifested strokes are only the "tip of the iceberg": it is estimated that the total number of stroke patients is about 14 times greater than the currently applied reactive medical approach is capable to identify and manage. Specifically, lacunar stroke (LS), which is characteristic for silent brain infarction, represents up to 30% of all ischemic strokes. Silent LS, which is diagnosed mainly by routine health check-up and autopsy in individuals without stroke history, has a reported prevalence of silent brain infarction up to 55% in the investigated populations. To this end, silent brain infarction is an independent predictor of ischemic stroke. Further, small vessel disease and silent lacunar brain infarction are considered strong contributors to cognitive impairments, dementia, depression and suicide, amongst others in the general population. In sub-populations such as diabetes mellitus type 2, proliferative diabetic retinopathy is an independent predictor of ischemic stroke. According to various statistical sources, cryptogenic strokes account for 15 to 40% of the entire stroke incidence. The question to consider here is, whether a cryptogenic stroke is fully referable to unidentifiable aetiology or rather to underestimated risks. Considering the latter, translational research might be of great clinical utility to realise innovative predictive and preventive approaches, potentially benefiting high risk individuals and society at large. In this position paper, the consortium has combined multi-professional expertise to provide clear statements towards the paradigm change from reactive to predictive, preventive and personalised medicine in stroke management, the crucial elements of which are:Consolidation of multi-disciplinary expertise including family medicine, predictive and in-depth diagnostics followed by the targeted primary and secondary (e.g. treated cancer) prevention of silent brain infarctionApplication of the health risk assessment focused on sub-optimal health conditions to effectively prevent health-to-disease transitionApplication of AI in medicine, machine learning and treatment algorithms tailored to robust biomarker patternsApplication of innovative screening programmes which adequately consider the needs of young populations.

Golubnitschaja Olga, Potuznik Pavel, Polivka Jiri, Pesta Martin, Kaverina Olga, Pieper Claus C, Kropp Martina, Thumann Gabriele, Erb Carl, Karabatsiakis Alexander, Stetkarova Ivana, Polivka Jiri, Costigliola Vincenzo

2022-Nov-17

Blood pressure, Blood–brain barrier breakdown, Body mass index, COVID-19, Cancer, Coagulation, Connective tissue impairments, Diabetes comorbidities, Endothelial dysfunction, Endothelin-1, Flammer Syndrome phenotype, Health policy, Health risk assessment, Health-to-disease transition, Hypoxia-reperfusion, Individualised protection, Ischemic stroke, Lacunar stroke, Mental health, Metastasis, Normal-tension glaucoma, Optic nerve degeneration, Paradigm change, Pre-pregnancy check-up, Predictive preventive personalised medicine (PPPM / 3PM), Primary care, Pro-inflammation, Retinal microvascular abnormalities, Screening, Secondary care, Silent brain infarct, Small vessel disease, Stress, Sub-optimal health, Systemic effects, Thromboembolism, Vascular stiffness, Vasospasm, Young populations

General General

Integration of machine learning prediction and heuristic optimization for mask delivery in COVID-19.

In Swarm and evolutionary computation

The novel coronavirus pneumonia (COVID-19) has created huge demands for medical masks that need to be delivered to a lot of demand points to protect citizens. The efficiency of delivery is critical to the prevention and control of the epidemic. However, the huge demands for masks and massive number of demand points scattered make the problem highly complex. Moreover, the actual demands are often obtained late, and hence the time duration for solution calculation and mask delivery is often very limited. Based on our practical experience of medical mask delivery in response to COVID-19 in China, we present a hybrid machine learning and heuristic optimization method, which uses a deep learning model to predict the demand of each region, schedules first-echelon vehicles to pre-distribute the predicted number of masks from depot(s) to regional facilities in advance, reassigns demand points among different regions to balance the deviations of predicted demands from actual demands, and finally routes second-echelon vehicles to efficiently deliver masks to the demand points in each region. For the subproblems of demand point reassignment and two-batch routing whose complexities are significantly lower, we propose variable neighborhood tabu search heuristics to efficiently solve them. Application of the proposed method in emergency mask delivery in three megacities in China during the peak of COVID-19 demonstrated its significant performance advantages over other methods without pre-distribution or reassignment. We also discuss key success factors and lessons learned to facilitate the extension of our method to a wider range of problems.

Chen Xin, Yan Hong-Fang, Zheng Yu-Jun, Karatas Mumtaz

2022-Nov-16

Heuristic optimization, Machine learning, Pre-distribution, Tabu search, Variable neighborhood, Vehicle routing

General General

Future forecasting prediction of Covid-19 using hybrid deep learning algorithm.

In Multimedia tools and applications

Due the quick spread of coronavirus disease 2019 (COVID-19), identification of that disease, prediction of mortality rate and recovery rate are considered as one of the critical challenges in the whole world. The occurrence of COVID-19 dissemination beyond the world is analyzed in this research and an artificial-intelligence (AI) based deep learning algorithm is suggested to detect positive cases of COVID19 patients, mortality rate and recovery rate using real-world datasets. Initially, the unwanted data like prepositions, links, hashtags etc., are removed using some pre-processing techniques. After that, term frequency inverse-term frequency (TF-IDF) andBag of Words (BoW) techniques are utilized to extract the features from pre-processed dataset. Then, Mayfly Optimization (MO) algorithm is performed to pick the relevant features from the set of features. Finally, two deep learning procedures, ResNet model and GoogleNet model, are hybridized to achieve the prediction process. Our system examines two different kinds of publicly available text datasets to identify COVID-19 disease as well as to predict mortality rate and recovery rate using those datasets. There are four different datasets are taken to analyse the performance, in which the proposed method achieves 97.56% accuracy which is 1.40% greater than Linear Regression (LR) and Multinomial Naive Bayesian (MNB), 3.39% higher than Random Forest (RF) and Stochastic gradient boosting (SGB) as well as 5.32% higher than Decision tree (DT) and Bagging techniques if first dataset. When compared to existing machine learning models, the simulation result indicates that a proposed hybrid deep learning method is valuable in corona virus identification and future mortality forecast study.

Yenurkar Ganesh, Mal Sandip

2022-Nov-18

As well as mayfly optimization (MO) algorithm, Corona disease, Feature extraction, Feature selection, GoogleNet, Hybrid deep learning model, ResNet

General General

ECG-COVID: An end-to-end deep model based on electrocardiogram for COVID-19 detection.

In Information sciences

The early and accurate detection of COVID-19 is vital nowadays to avoid the vast and rapid spread of this virus and ease lockdown restrictions. As a result, researchers developed methods to diagnose COVID-19. However, these methods have several limitations. Therefore, presenting new methods is essential to improve the diagnosis of COVID-19. Recently, investigation of the electrocardiogram (ECG) signals becoming an easy way to detect COVID-19 since the ECG process is non-invasive and easy to use. Therefore, we proposed in this paper a novel end-to-end deep learning model (ECG-COVID) based on ECG for COVID-19 detection. We employed several deep Convolutional Neural Networks (CNNs) on a dataset of 1109 ECG images, which is built for screening the perception of COVID-19 and cardiac patients. After that, we selected the most efficient model as our model for evaluation. The proposed model is end-to-end where the input ECG images are fed directly to the model for the final decision without using any additional stages. The proposed method achieved an average accuracy of 98.81%, Precision of 98.8%, Sensitivity of 98.8% and, F1-score of 98.81% for COVID-19 detection. As cases of corona continue to rise and hospitalizations continue again, hospitals may find our study helpful when dealing with these patients who did not get significantly worse.

Sakr Ahmed S, Pławiak Paweł, Tadeusiewicz Ryszard, Pławiak Joanna, Sakr Mohamed, Hammad Mohamed

2023-Jan

CNN, COVID-19, Deep learning, ECG, End-to-end

General General

Explaining COVID-19 diagnosis with Taylor decompositions.

In Neural computing & applications

The COVID-19 pandemic has devastated the entire globe since its first appearance at the end of 2019. Although vaccines are now in production, the number of contaminations remains high, thus increasing the number of specialized personnel that can analyze clinical exams and points out the final diagnosis. Computed tomography and X-ray images are the primary sources for computer-aided COVID-19 diagnosis, but we still lack better interpretability of such automated decision-making mechanisms. This manuscript presents an insightful comparison of three approaches based on explainable artificial intelligence (XAI) to light up interpretability in the context of COVID-19 diagnosis using deep networks: Composite Layer-wise Propagation, Single Taylor Decomposition, and Deep Taylor Decomposition. Two deep networks have been used as the backbones to assess the explanation skills of the XAI approaches mentioned above: VGG11 and VGG16. We hope that such work can be used as a basis for further research on XAI and COVID-19 diagnosis for each approach figures its own positive and negative points.

Hassan Mohammad Mehedi, AlQahtani Salman A, Alelaiwi Abdulhameed, Papa João P

2022-Nov-17

COVID-19, Deep Taylor expansion, Explainable artificial intelligence, Machine learning

General General

Fragment-Based Hit Discovery via Unsupervised Learning of Fragment-Protein Complexes

bioRxiv Preprint

The process of finding molecules that bind to a target protein is a challenging first step in drug discovery. Crystallographic fragment screening is a strategy based on elucidating binding modes of small polar compounds and then building potency by expanding or merging them. Recent advances in high-throughput crystallography enable screening of large fragment libraries, reading out dense ensembles of fragments spanning the binding site. However, fragments typically have low affinity thus the road to potency is often long and fraught with false starts. Here, we take advantage of high-throughput crystallography to reframe fragment-based hit discovery as a denoising problem -- identifying significant pharmacophore distributions from a fragment ensemble amid noise due to weak binders -- and employ an unsupervised machine learning method to tackle this problem. Our method screens potential molecules by evaluating whether they recapitulate those fragment-derived pharmacophore distributions. We retrospectively validated our approach on an open science campaign against SARS-CoV-2 main protease (Mpro), showing that our method can distinguish active compounds from inactive ones using only structural data of fragment-protein complexes, without any activity data. Further, we prospectively found novel hits for Mpro and the Mac1 domain of SARS-CoV-2 non-structural protein 3. More broadly, our results demonstrate how unsupervised machine learning helps interpret high throughput crystallography data to rapidly discover of potent chemical modulators of protein function.

McCorkindale, W. J.; Ahel, I.; Barr, H.; Correy, G. J.; Fraser, J. S.; London, N.; Schuller, M.; Shurrush, K.; Lee, A. A.

2022-11-24

Public Health Public Health

Vocal biomarker predicts fatigue in people with COVID-19: results from the prospective Predi-COVID cohort study.

In BMJ open

OBJECTIVE : To develop a vocal biomarker for fatigue monitoring in people with COVID-19.

DESIGN : Prospective cohort study.

SETTING : Predi-COVID data between May 2020 and May 2021.

PARTICIPANTS : A total of 1772 voice recordings were used to train an AI-based algorithm to predict fatigue, stratified by gender and smartphone's operating system (Android/iOS). The recordings were collected from 296 participants tracked for 2 weeks following SARS-CoV-2 infection.

PRIMARY AND SECONDARY OUTCOME MEASURES : Four machine learning algorithms (logistic regression, k-nearest neighbours, support vector machine and soft voting classifier) were used to train and derive the fatigue vocal biomarker. The models were evaluated based on the following metrics: area under the curve (AUC), accuracy, F1-score, precision and recall. The Brier score was also used to evaluate the models' calibrations.

RESULTS : The final study population included 56% of women and had a mean (±SD) age of 40 (±13) years. Women were more likely to report fatigue (p<0.001). We developed four models for Android female, Android male, iOS female and iOS male users with a weighted AUC of 86%, 82%, 79%, 85% and a mean Brier Score of 0.15, 0.12, 0.17, 0.12, respectively. The vocal biomarker derived from the prediction models successfully discriminated COVID-19 participants with and without fatigue.

CONCLUSIONS : This study demonstrates the feasibility of identifying and remotely monitoring fatigue thanks to voice. Vocal biomarkers, digitally integrated into telemedicine technologies, are expected to improve the monitoring of people with COVID-19 or Long-COVID.

TRIAL REGISTRATION NUMBER : NCT04380987.

Elbéji Abir, Zhang Lu, Higa Eduardo, Fischer Aurélie, Despotovic Vladimir, Nazarov Petr V, Aguayo Gloria, Fagherazzi Guy

2022-Nov-22

COVID-19, Health informatics, Public health

Pathology Pathology

Risk Stratification of COVID-19 Using Routine Laboratory Tests: A Machine Learning Approach.

In Infectious disease reports

The COVID-19 pandemic placed significant stress on an already overburdened health system. The diagnosis was based on detection of a positive RT-PCR test, which may be delayed when there is peak demand for testing. Rapid risk stratification of high-risk patients allows for the prioritization of resources for patient care. The study aims were to classify patients as severe or not severe based on outcomes using machine learning on routine laboratory tests. Data were extracted for all individuals who had at least one SARS-CoV-2 PCR test conducted via the NHLS between the periods of 1 March 2020 to 7 July 2020. Exclusion criteria: those 18 years, and those with indeterminate PCR tests. Results for 15437 patients (3301 positive and 12,136 negative) were used to fit six machine learning models, namely the logistic regression (LR) (the base model), decision trees (DT), random forest (RF), extreme gradient boosting (XGB), convolutional neural network (CNN) and self-normalising neural network (SNN). Model development was carried out by splitting the data into training and testing set of a ratio 70:30, together with a 10-fold cross-validation re-sampling technique. For risk stratification, admission to high care or ICU was the outcome for severe disease. Performance of the models varied: sensitivity was best for RF at 75% and accuracy of 75% for CNN. The area under the curve ranged from 57% for CNN to 75% for RF. RF and SNN were the best-performing models. Machine Learning (ML) can be incorporated into the laboratory information system and offers promise for early identification and risk stratification of COVID-19 patients, particularly in areas of resource-poor settings.

Mlambo Farai, Chironda Cyril, George Jaya

2022-Nov-21

COVID-19, laboratory tests, machine learning, risk stratification

Public Health Public Health

MonkeyPox2022Tweets: A Large-Scale Twitter Dataset on the 2022 Monkeypox Outbreak, Findings from Analysis of Tweets, and Open Research Questions.

In Infectious disease reports

The mining of Tweets to develop datasets on recent issues, global challenges, pandemics, virus outbreaks, emerging technologies, and trending matters has been of significant interest to the scientific community in the recent past, as such datasets serve as a rich data resource for the investigation of different research questions. Furthermore, the virus outbreaks of the past, such as COVID-19, Ebola, Zika virus, and flu, just to name a few, were associated with various works related to the analysis of the multimodal components of Tweets to infer the different characteristics of conversations on Twitter related to these respective outbreaks. The ongoing outbreak of the monkeypox virus, declared a Global Public Health Emergency (GPHE) by the World Health Organization (WHO), has resulted in a surge of conversations about this outbreak on Twitter, which is resulting in the generation of tremendous amounts of Big Data. There has been no prior work in this field thus far that has focused on mining such conversations to develop a Twitter dataset. Furthermore, no prior work has focused on performing a comprehensive analysis of Tweets about this ongoing outbreak. To address these challenges, this work makes three scientific contributions to this field. First, it presents an open-access dataset of 556,427 Tweets about monkeypox that have been posted on Twitter since the first detected case of this outbreak. A comparative study is also presented that compares this dataset with 36 prior works in this field that focused on the development of Twitter datasets to further uphold the novelty, relevance, and usefulness of this dataset. Second, the paper reports the results of a comprehensive analysis of the Tweets of this dataset. This analysis presents several novel findings; for instance, out of all the 34 languages supported by Twitter, English has been the most used language to post Tweets about monkeypox, about 40,000 Tweets related to monkeypox were posted on the day WHO declared monkeypox as a GPHE, a total of 5470 distinct hashtags have been used on Twitter about this outbreak out of which #monkeypox is the most used hashtag, and Twitter for iPhone has been the leading source of Tweets about the outbreak. The sentiment analysis of the Tweets was also performed, and the results show that despite a lot of discussions, debate, opinions, information, and misinformation, on Twitter on various topics in this regard, such as monkeypox and the LGBTQI+ community, monkeypox and COVID-19, vaccines for monkeypox, etc., "neutral" sentiment was present in most of the Tweets. It was followed by "negative" and "positive" sentiments, respectively. Finally, to support research and development in this field, the paper presents a list of 50 open research questions related to the outbreak in the areas of Big Data, Data Mining, Natural Language Processing, and Machine Learning that may be investigated based on this dataset.

Thakur Nirmalya

2022-Nov-14

big data, data analysis, data mining, dataset, machine learning, monkeypox, natural language processing, social media, tweets, twitter

General General

Online Dynamic Reliability Evaluation of Wind Turbines based on Drone-assisted Monitoring

ArXiv Preprint

The offshore wind energy is increasingly becoming an attractive source of energy due to having lower environmental impact. Effective operation and maintenance that ensures the maximum availability of the energy generation process using offshore facilities and minimal production cost are two key factors to improve the competitiveness of this energy source over other traditional sources of energy. Condition monitoring systems are widely used for health management of offshore wind farms to have improved operation and maintenance. Reliability of the wind farms are increasingly being evaluated to aid in the maintenance process and thereby to improve the availability of the farms. However, much of the reliability analysis is performed offline based on statistical data. In this article, we propose a drone-assisted monitoring based method for online reliability evaluation of wind turbines. A blade system of a wind turbine is used as an illustrative example to demonstrate the proposed approach.

Sohag Kabir, Koorosh Aslansefat, Prosanta Gope, Felician Campean, Yiannis Papadopoulos

2022-11-23

General General

Predicting drug-target binding affinity through molecule representation block based on multi-head attention and skip connection.

In Briefings in bioinformatics

Exiting computational models for drug-target binding affinity prediction have much room for improvement in prediction accuracy, robustness and generalization ability. Most deep learning models lack interpretability analysis and few studies provide application examples. Based on these observations, we presented a novel model named Molecule Representation Block-based Drug-Target binding Affinity prediction (MRBDTA). MRBDTA is composed of embedding and positional encoding, molecule representation block and interaction learning module. The advantages of MRBDTA are reflected in three aspects: (i) developing Trans block to extract molecule features through improving the encoder of transformer, (ii) introducing skip connection at encoder level in Trans block and (iii) enhancing the ability to capture interaction sites between proteins and drugs. The test results on two benchmark datasets manifest that MRBDTA achieves the best performance compared with 11 state-of-the-art models. Besides, through replacing Trans block with single Trans encoder and removing skip connection in Trans block, we verified that Trans block and skip connection could effectively improve the prediction accuracy and reliability of MRBDTA. Then, relying on multi-head attention mechanism, we performed interpretability analysis to illustrate that MRBDTA can correctly capture part of interaction sites between proteins and drugs. In case studies, we firstly employed MRBDTA to predict binding affinities between Food and Drug Administration-approved drugs and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) replication-related proteins. Secondly, we compared true binding affinities between 3C-like proteinase and 185 drugs with those predicted by MRBDTA. The final results of case studies reveal reliable performance of MRBDTA in drug design for SARS-CoV-2.

Zhang Li, Wang Chun-Chun, Chen Xing

2022-Nov-19

SARS-CoV-2, computational model, drug–target binding affinity, molecule representation block, multi-head attention, skip connection

Radiology Radiology

RoentGen: Vision-Language Foundation Model for Chest X-ray Generation

ArXiv Preprint

Multimodal models trained on large natural image-text pair datasets have exhibited astounding abilities in generating high-quality images. Medical imaging data is fundamentally different to natural images, and the language used to succinctly capture relevant details in medical data uses a different, narrow but semantically rich, domain-specific vocabulary. Not surprisingly, multi-modal models trained on natural image-text pairs do not tend to generalize well to the medical domain. Developing generative imaging models faithfully representing medical concepts while providing compositional diversity could mitigate the existing paucity of high-quality, annotated medical imaging datasets. In this work, we develop a strategy to overcome the large natural-medical distributional shift by adapting a pre-trained latent diffusion model on a corpus of publicly available chest x-rays (CXR) and their corresponding radiology (text) reports. We investigate the model's ability to generate high-fidelity, diverse synthetic CXR conditioned on text prompts. We assess the model outputs quantitatively using image quality metrics, and evaluate image quality and text-image alignment by human domain experts. We present evidence that the resulting model (RoentGen) is able to create visually convincing, diverse synthetic CXR images, and that the output can be controlled to a new extent by using free-form text prompts including radiology-specific language. Fine-tuning this model on a fixed training set and using it as a data augmentation method, we measure a 5% improvement of a classifier trained jointly on synthetic and real images, and a 3% improvement when trained on a larger but purely synthetic training set. Finally, we observe that this fine-tuning distills in-domain knowledge in the text-encoder and can improve its representation capabilities of certain diseases like pneumothorax by 25%.

Pierre Chambon, Christian Bluethgen, Jean-Benoit Delbrouck, Rogier Van der Sluijs, Małgorzata Połacin, Juan Manuel Zambrano Chaves, Tanishq Mathew Abraham, Shivanshu Purohit, Curtis P. Langlotz, Akshay Chaudhari

2022-11-23

Public Health Public Health

CNN-LSTM deep learning based forecasting model for COVID-19 infection cases in Nigeria, South Africa and Botswana.

In Health and technology

Background : COVID-19 pandemic has indeed plunged the global community especially African countries into an alarming difficult situation culminating into a great deal amounts of catastrophes such as economic recession, political instability and loss of jobs. The pandemic spreads exponentially and causes loss of lives. Following the outbreak of the omicron new variant of concern, forecasting and identification of the COVID-19 infection cases is very vital for government at various levels. Hence, having knowledge of the spread at a particular point in time, swift actions can be taken by government at various levels with a view to accordingly formulate new policies and modalities towards minimizing the trajectory of the consequences of COVID-19 pandemic to both public health and economic sectors.

Methods : Here, a potent combination of Convolutional Neural Network (CNN) learning algorithm along with Long Short Term Memory (LSTM) learning algorithm has been proposed in this work in order to produce a hybrid of a deep learning algorithm Convolutional Neural Network - Long Short Term Memory (CNN-LSTM) for forecasting COVID-19 infection cases particularly in Nigeria, South Africa and Botswana. Forecasting models for COVID-19 infection cases in Nigeria, South Africa and Botswana, were developed for 10 days using deep learning-based approaches namely CNN, LSTM and CNN-LSTM deep learning algorithm respectively.

Results : The models were evaluated on the basis of four standard performance evaluation metrics which include accuracy, MSE, MAE and RMSE respectively. However, the CNN-LSTM deep learning-based forecasting model achieved the best accuracy of 98.30%, 97.60%, and 97.74% for Nigeria, South Africa and Botswana respectively; and in the same manner, achieved lesser MSE, MAE and RMSE values compared to models developed with CNN and LSTM respectively.

Conclusions : Taken together, the CNN-LSTM deep learning-based forecasting model for COVID-19 infection cases in Nigeria, South Africa and Botswana dramatically surpasses the two other DL based forecasting models (CNN and LSTM) for COVID-19 infection cases in Nigeria, South Africa and Botswana in terms of not only the best accuracy of with 98.30%, 97.60%, and 97.74% but also in terms of lesser MSE, MAE and RMSE.

Muhammad L J, Haruna Ahmed Abba, Sharif Usman Sani, Mohammed Mohammed Bappah

2022

COVID-19, Deep Learning, Forecasting Model, Infection, Omicron Variant of Concern

General General

Machine learning for optimizing daily COVID-19 vaccine dissemination to combat the pandemic.

In Health and technology

Introduction : Vaccines are the most important instrument for bringing the pandemic to a close and saving lives and helping to reduce the risks of infection. It is important that everyone has equal access to immunizations that are both safe and effective. There is no one who is safe until everyone gets vaccinated. COVID-19 vaccinations are a game-changer in the fight against diseases. In addition to examining attitudes toward these vaccines in Africa, Asia, Oceania, Europe, North America, and South America, the purpose of this paper is to predict the acceptability of COVID-19 vaccines and study their predictors.

Materials and methods : Kaggle datasets are used to estimate the prediction outcomes of the daily COVID-19 vaccination to prevent a pandemic. The Kaggle data sets are classified into training and testing datasets. The training dataset is comprised of COVID-19 daily data from the 13th of December 2020 to the 13th of June 2021, while the testing dataset is comprised of COVID-19 daily data from the 14th of June 2021 to the 14th of October 2021. For the prediction of daily COVID-19 vaccination, four well-known machine learning algorithms were described and used in this study: CUBIST, Gaussian Process (GAUSS), Elastic Net (ENET), Spikes, and Slab (SPIKES).

Results : Among the models considered in this paper, CUBIST has the best prediction accuracy in terms of Mean Absolute Scaled Error (MASE) of 9.7368 for Asia, 2.8901 for America, 13.2169 for Oceania, and 3.9510 for South America respectively.

Conclusion : This research shows that machine learning can be of great benefit for optimizing daily immunization of citizens across the globe. And if used properly, it can help decision makers and health administrators to comprehend immunization rates and create strategies to enhance them.

Oyewola David Opeoluwa, Dada Emmanuel Gbenga, Misra Sanjay

2022

COVID-19, Elastic net (ENET), Gaussian process (GAUSS), Machine learning, Spikes and slab (SPIKES), Vaccination

Public Health Public Health

Using Natural Language Processing to Explore Mental Health Insights From UK Tweets During the COVID-19 Pandemic: Infodemiology Study.

In JMIR infodemiology

Background : There is need to consider the value of soft intelligence, leveraged using accessible natural language processing (NLP) tools, as a source of analyzed evidence to support public health research outputs and decision-making.

Objective : The aim of this study was to explore the value of soft intelligence analyzed using NLP. As a case study, we selected and used a commercially available NLP platform to identify, collect, and interrogate a large collection of UK tweets relating to mental health during the COVID-19 pandemic.

Methods : A search strategy comprised of a list of terms related to mental health, COVID-19, and lockdown restrictions was developed to prospectively collate relevant tweets via Twitter's advanced search application programming interface over a 24-week period. We deployed a readily and commercially available NLP platform to explore tweet frequency and sentiment across the United Kingdom and identify key topics of discussion. A series of keyword filters were used to clean the initial data retrieved and also set up to track specific mental health problems. All collated tweets were anonymized.

Results : We identified and analyzed 286,902 tweets posted from UK user accounts from July 23, 2020 to January 6, 2021. The average sentiment score was 50%, suggesting overall neutral sentiment across all tweets over the study period. Major fluctuations in volume (between 12,622 and 51,340) and sentiment (between 25% and 49%) appeared to coincide with key changes to any local and/or national social distancing measures. Tweets around mental health were polarizing, discussed with both positive and negative sentiment. Key topics of consistent discussion over the study period included the impact of the pandemic on people's mental health (both positively and negatively), fear and anxiety over lockdowns, and anger and mistrust toward the government.

Conclusions : Using an NLP platform, we were able to rapidly mine and analyze emerging health-related insights from UK tweets into how the pandemic may be impacting people's mental health and well-being. This type of real-time analyzed evidence could act as a useful intelligence source that agencies, local leaders, and health care decision makers can potentially draw from, particularly during a health crisis.

Marshall Christopher, Lanyi Kate, Green Rhiannon, Wilkins Georgina C, Pearson Fiona, Craig Dawn

COVID-19, Twitter, artificial intelligence, lockdown, machine learning, mental health, natural language processing, sentiment, soft intelligence

Pathology Pathology

The need for measurement science in digital pathology.

In Journal of pathology informatics ; h5-index 23.0

Background : Pathology services experienced a surge in demand during the COVID-19 pandemic. Digitalisation of pathology workflows can help to increase throughput, yet many existing digitalisation solutions use non-standardised workflows captured in proprietary data formats and processed by black-box software, yielding data of varying quality. This study presents the views of a UK-led expert group on the barriers to adoption and the required input of measurement science to improve current practices in digital pathology.

Methods : With an aim to support the UK's efforts in digitalisation of pathology services, this study comprised: (1) a review of existing evidence, (2) an online survey of domain experts, and (3) a workshop with 42 representatives from healthcare, regulatory bodies, pharmaceutical industry, academia, equipment, and software manufacturers. The discussion topics included sample processing, data interoperability, image analysis, equipment calibration, and use of novel imaging modalities.

Findings : The lack of data interoperability within the digital pathology workflows hinders data lookup and navigation, according to 80% of attendees. All participants stressed the importance of integrating imaging and non-imaging data for diagnosis, while 80% saw data integration as a priority challenge. 90% identified the benefits of artificial intelligence and machine learning, but identified the need for training and sound performance metrics.Methods for calibration and providing traceability were seen as essential to establish harmonised, reproducible sample processing, and image acquisition pipelines. Vendor-neutral data standards were seen as a "must-have" for providing meaningful data for downstream analysis. Users and vendors need good practice guidance on evaluation of uncertainty, fitness-for-purpose, and reproducibility of artificial intelligence/machine learning tools. All of the above needs to be accompanied by an upskilling of the pathology workforce.

Conclusions : Digital pathology requires interoperable data formats, reproducible and comparable laboratory workflows, and trustworthy computer analysis software. Despite high interest in the use of novel imaging techniques and artificial intelligence tools, their adoption is slowed down by the lack of guidance and evaluation tools to assess the suitability of these techniques for specific clinical question. Measurement science expertise in uncertainty estimation, standardisation, reference materials, and calibration can help establishing reproducibility and comparability between laboratory procedures, yielding high quality data and providing higher confidence in diagnosis.

Romanchikova Marina, Thomas Spencer, Dexter Alex, Shaw Mike, Partarrieau Ignacio, Smith Nadia, Venton Jenny, Adeogun Michael, Brettle David, Turpin Robert James

2022-Nov-10

Artificial intelligence, Calibration, DICOM, Digital pathology, FAIR principles, Machine learning, Metadata, Metrology, Standards, Whole slide imaging

General General

Using machine learning models to predict the duration of the recovery of COVID-19 patients hospitalized in Fangcang shelter hospital during the Omicron BA. 2.2 pandemic.

In Frontiers in medicine

Background : Factors that may influence the recovery of patients with confirmed SARS-CoV-2 infection hospitalized in the Fangcang shelter were explored, and machine learning models were constructed to predict the duration of recovery during the Omicron BA. 2.2 pandemic.

Methods : A retrospective study was conducted at Hongqiao National Exhibition and Convention Center Fangcang shelter (Shanghai, China) from April 9, 2022 to April 25, 2022. The demographics, clinical data, inoculation history, and recovery information of the 13,162 enrolled participants were collected. A multivariable logistic regression model was used to identify independent factors associated with 7-day recovery and 14-day recovery. Machine learning algorithms (DT, SVM, RF, DT/AdaBoost, AdaBoost, SMOTEENN/DT, SMOTEENN/SVM, SMOTEENN/RF, SMOTEENN+DT/AdaBoost, and SMOTEENN/AdaBoost) were used to build models for predicting 7-day and 14-day recovery.

Results : Of the 13,162 patients in the study, the median duration of recovery was 8 days (interquartile range IQR, 6-10 d), 41.31% recovered within 7 days, and 94.83% recovered within 14 days. Univariate analysis showed that the administrative region, age, cough medicine, comorbidities, diabetes, coronary artery disease (CAD), hypertension, number of comorbidities, CT value of the ORF gene, CT value of the N gene, ratio of ORF/IC, and ratio of N/IC were associated with a duration of recovery within 7 days. Age, gender, vaccination dose, cough medicine, comorbidities, diabetes, CAD, hypertension, number of comorbidities, CT value of the ORF gene, CT value of the N gene, ratio of ORF/IC, and ratio of N/IC were related to a duration of recovery within 14 days. In the multivariable analysis, the receipt of two doses of the vaccination vs. unvaccinated (OR = 1.118, 95% CI = 1.003-1.248; p = 0.045), receipt of three doses of the vaccination vs. unvaccinated (OR = 1.114, 95% CI = 1.004-1.236; p = 0.043), diabetes (OR = 0.383, 95% CI = 0.194-0.749; p = 0.005), CAD (OR = 0.107, 95% CI = 0.016-0.421; p = 0.005), hypertension (OR = 0.371, 95% CI = 0.202-0.674; p = 0.001), and ratio of N/IC (OR = 3.686, 95% CI = 2.939-4.629; p < 0.001) were significantly and independently associated with a duration of recovery within 7 days. Gender (OR = 0.736, 95% CI = 0.63-0.861; p < 0.001), age (30-70) (OR = 0.738, 95% CI = 0.594-0.911; p < 0.001), age (>70) (OR = 0.38, 95% CI = 0292-0.494; p < 0.001), receipt of three doses of the vaccination vs. unvaccinated (OR = 1.391, 95% CI = 1.12-1.719; p = 0.0033), cough medicine (OR = 1.509, 95% CI = 1.075-2.19; p = 0.023), and symptoms (OR = 1.619, 95% CI = 1.306-2.028; p < 0.001) were significantly and independently associated with a duration of recovery within 14 days. The SMOTEEN/RF algorithm performed best, with an accuracy of 90.32%, sensitivity of 92.22%, specificity of 88.31%, F1 score of 90.71%, and AUC of 89.75% for the 7-day recovery prediction; and an accuracy of 93.81%, sensitivity of 93.40%, specificity of 93.81%, F1 score of 93.42%, and AUC of 93.53% for the 14-day recovery prediction.

Conclusion : Age and vaccination dose were factors robustly associated with accelerated recovery both on day 7 and day 14 from the onset of disease during the Omicron BA. 2.2 wave. The results suggest that the SMOTEEN/RF-based model could be used to predict the probability of 7-day and 14-day recovery from the Omicron variant of SARS-CoV-2 infection for COVID-19 prevention and control policy in other regions or countries. This may also help to generate external validation for the model.

Xu Yu, Ye Wei, Song Qiuyue, Shen Linlin, Liu Yu, Guo Yuhang, Liu Gang, Wu Hongmei, Wang Xia, Sun Xiaorong, Bai Li, Luo Chunmei, Liao Tongquan, Chen Hao, Song Caiping, Huang Chunji, Wu Yazhou, Xu Zhi

2022

COVID-19, Fangcang shelter, machine learning model, omicron, vaccination

General General

Efficient-ECGNet framework for COVID-19 classification and correlation prediction with the cardio disease through electrocardiogram medical imaging.

In Frontiers in medicine

In the last 2 years, we have witnessed multiple waves of coronavirus that affected millions of people around the globe. The proper cure for COVID-19 has not been diagnosed as vaccinated people also got infected with this disease. Precise and timely detection of COVID-19 can save human lives and protect them from complicated treatment procedures. Researchers have employed several medical imaging modalities like CT-Scan and X-ray for COVID-19 detection, however, little concentration is invested in the ECG imaging analysis. ECGs are quickly available image modality in comparison to CT-Scan and X-ray, therefore, we use them for diagnosing COVID-19. Efficient and effective detection of COVID-19 from the ECG signal is a complex and time-taking task, as researchers usually convert them into numeric values before applying any method which ultimately increases the computational burden. In this work, we tried to overcome these challenges by directly employing the ECG images in a deep-learning (DL)-based approach. More specifically, we introduce an Efficient-ECGNet method that presents an improved version of the EfficientNetV2-B4 model with additional dense layers and is capable of accurately classifying the ECG images into healthy, COVID-19, myocardial infarction (MI), abnormal heartbeats (AHB), and patients with Previous History of Myocardial Infarction (PMI) classes. Moreover, we introduce a module to measure the similarity of COVID-19-affected ECG images with the rest of the diseases. To the best of our knowledge, this is the first effort to approximate the correlation of COVID-19 patients with those having any previous or current history of cardio or respiratory disease. Further, we generate the heatmaps to demonstrate the accurate key-points computation ability of our method. We have performed extensive experimentation on a publicly available dataset to show the robustness of the proposed approach and confirmed that the Efficient-ECGNet framework is reliable to classify the ECG-based COVID-19 samples.

Nawaz Marriam, Nazir Tahira, Javed Ali, Malik Khalid Mahmood, Saudagar Abdul Khader Jilani, Khan Muhammad Badruddin, Abul Hasanat Mozaherul Hoque, AlTameem Abdullah, AlKhathami Mohammed

2022

COVID-19, ECG, Efficient-ECGNet, computer vision, deep learning, medical imaging

General General

Monitoring the security of audio biomedical signals communications in wearable IoT healthcare.

In Digital communications and networks

The COVID-19 pandemic has imposed new challenges on the healthcare industry as hospital staff are exposed to a massive coronavirus load when registering new patients, taking temperatures, and providing care. The Ebola epidemic of 2014 is another example of a pandemic which a hospital in New York decided to use an audio-based communication system to protect nurses. This idea quickly turned into an Internet of Things (IoT) healthcare solution to help to communicate with patients remotely. However, it has grabbed the attention of criminals who use this medium as a cover for secret communication. The merging of signal processing and machine-learning techniques has led to the development of steganalyzers with very higher efficiencies, but since the statistical properties of normal audio files differ from those of purely speech audio files, the current steganalysis practices are not efficient enough for this type of content. This research considers the Percent of Equal Adjacent Samples (PEAS) feature for speech steganalysis. This feature efficiently discriminates the least significant bit stego speech samples from clean ones with a single analysis dimension. A sensitivity of 99.82% was achieved for the steganalysis of 50% embedded stego instances using a classifier based on the Gaussian membership function.

Yazdanpanah Saeid, Chaeikar Saman Shojae, Jolfaei Alireza

2022-Nov-14

Audio security, Audio signal processing, Data hiding, Healthcare data, IoT security

Public Health Public Health

Managing healthcare supply chain through Artificial Intelligence (AI): A study of critical success factors.

In Computers & industrial engineering

Healthcare is one of the most critical sectors due to its importance in handling public health. With the outbreak of various diseases, more recently during Covid-19, this sector has gained further attention. The pandemic has exposed vulnerabilities in the healthcare supply chain (HSC). Recent advancements like the adoption of various advanced technologies viz. AI and Industry 4.0 in the healthcare supply chain are turning out to be game-changers. This study focuses on identifying critical success factors (CSFs) for AI adoption in HSC in the emerging economy context. Rough SWARA is used for ranking CSFs of AI adoption in HSC. Results indicate that technological (TEC) factors are the most influential factor that impacts the adoption of AI in HSC in the context of emerging economies, followed by institutional or environmental (INT), human (HUM), and organizational (ORG) dimensions.

Kumar Ashwani, Mani Venkatesh, Jain Vranda, Gupta Himanshu, Venkatesh V G

2022-Nov-15

Artificial Intelligence, Healthcare, SWARA, supply chain

General General

Performance drift in a mortality prediction algorithm among patients with cancer during the SARS-CoV-2 pandemic.

In Journal of the American Medical Informatics Association : JAMIA

Sudden changes in health care utilization during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic may have impacted the performance of clinical predictive models that were trained prior to the pandemic. In this study, we evaluated the performance over time of a machine learning, electronic health record-based mortality prediction algorithm currently used in clinical practice to identify patients with cancer who may benefit from early advance care planning conversations. We show that during the pandemic period, algorithm identification of high-risk patients had a substantial and sustained decline. Decreases in laboratory utilization during the peak of the pandemic may have contributed to drift. Calibration and overall discrimination did not markedly decline during the pandemic. This argues for careful attention to the performance and retraining of predictive algorithms that use inputs from the pandemic period.

Parikh Ravi B, Zhang Yichen, Kolla Likhitha, Chivers Corey, Courtright Katherine R, Zhu Jingsan, Navathe Amol S, Chen Jinbo

2022-Nov-21

SARS-CoV-2, algorithm drift, cancer, machine learning, mortality

Public Health Public Health

SARS-CoV-2 seroprevalence, cumulative infections, and immunity to symptomatic infection - A multistage national household survey and modelling study, Dominican Republic, June-October 2021.

In Lancet regional health. Americas

Background : Population-level SARS-CoV-2 immunological protection is poorly understood but can guide vaccination and non-pharmaceutical intervention priorities. Our objective was to characterise cumulative infections and immunological protection in the Dominican Republic.

Methods : Household members ≥5 years were enrolled in a three-stage national household cluster serosurvey in the Dominican Republic. We measured pan-immunoglobulin antibodies against the SARS-CoV-2 spike (anti-S) and nucleocapsid glycoproteins, and pseudovirus neutralising activity against the ancestral and B.1.617.2 (Delta) strains. Seroprevalence and cumulative prior infections were weighted and adjusted for assay performance and seroreversion. Binary classification machine learning methods and pseudovirus neutralising correlates of protection were used to estimate 50% and 80% protection against symptomatic infection.

Findings : Between 30 Jun and 12 Oct 2021 we enrolled 6683 individuals from 3832 households. We estimate that 85.0% (CI 82.1-88.0) of the ≥5 years population had been immunologically exposed and 77.5% (CI 71.3-83) had been previously infected. Protective immunity sufficient to provide at least 50% protection against symptomatic SARS-CoV-2 infection was estimated in 78.1% (CI 74.3-82) and 66.3% (CI 62.8-70) of the population for the ancestral and Delta strains respectively. Younger (5-14 years, OR 0.47 [CI 0.36-0.61]) and older (≥75-years, 0.40 [CI 0.28-0.56]) age, working outdoors (0.53 [0.39-0.73]), smoking (0.66 [0.52-0.84]), urban setting (1.30 [1.14-1.49]), and three vs no vaccine doses (18.41 [10.69-35.04]) were associated with 50% protection against the ancestral strain.

Interpretation : Cumulative infections substantially exceeded prior estimates and overall immunological exposure was high. After controlling for confounders, markedly lower immunological protection was observed to the ancestral and Delta strains across certain subgroups, findings that can guide public health interventions and may be generalisable to other settings and viral strains.

Funding : This study was funded by the US CDC.

Nilles Eric J, Paulino Cecilia Then, de St Aubin Michael, Restrepo Angela Cadavid, Mayfield Helen, Dumas Devan, Finch Emilie, Garnier Salome, Etienne Marie Caroline, Iselin Louisa, Duke William, Jarolim Petr, Oasan Timothy, Yu Jingyou, Wan Huahua, Peña Farah, Iihoshi Naomi, Abdalla Gabriela, Lopez Beatriz, Cruz Lucia de la, Henríquez Bernarda, Espinosa-Bode Andres, Puello Yosanly Cornelio, Durski Kara, Baldwin Margaret, Baez Amado Alejandro, Merchant Roland C, Barouch Dan H, Skewes-Ramm Ronald, Gutiérrez Emily Zielinski, Kucharski Adam, Lau Colleen L

2022-Dec

Public Health Public Health

Novel favipiravir pattern-based learning model for automated detection of specific language impairment disorder using vowels.

In Neural computing & applications

Specific language impairment (SLI) is one of the most common diseases in children, and early diagnosis can help to obtain better timely therapy economically. It is difficult and time-consuming for clinicians to accurately detect SLI through standard clinical assessments. Hence, machine learning algorithms have been developed to assist in the accurate diagnosis of SLI. This work aims to investigate the graph of the favipiravir molecule-based feature extraction function and propose an accurate SLI detection model using vowels. We proposed a novel handcrafted machine learning framework. This architecture comprises the favipiravir molecular structure pattern, statistical feature extractor, wavelet packet decomposition (WPD), iterative neighborhood component analysis (INCA), and support vector machine (SVM) classifier. Two feature extraction models, statistical and textural, are employed in the handcrafted feature generation methodology. A new nature-inspired graph-based feature extractor that uses the chemical depiction of the favipiravir (favipiravir became popular with the COVID-19 pandemic) is employed for feature extraction. Finally, the proposed favipiravir pattern, statistical feature extractor, and wavelet packet decomposition are used to create a feature vector. Moreover, a statistical feature extractor is used in this work. The WPD generates multilevel features, and the most meaningful features are selected using the NCA feature selector. Finally, these chosen features are fed to SVM classifier for automated classification. Two validation methods, (i) leave one subject out (LOSO) and (ii) tenfold cross-validations (CV), are used to obtain robust classification results. Our proposed favipiravir pattern-based model developed using a vowel dataset can detect SLI children with an accuracy of 99.87% and 98.86% using tenfold and LOSO CV strategies, respectively. These results demonstrated the high vowel classification ability of the proposed favipiravir pattern-based model.

Barua Prabal Datta, Aydemir Emrah, Dogan Sengul, Erten Mehmet, Kaysi Feyzi, Tuncer Turker, Fujita Hamido, Palmer Elizabeth, Acharya U Rajendra

2022-Nov-13

Favipiravir pattern, Molecular graph-based feature extraction, Specific language impairment, Vowel-based disease diagnosis

General General

Framework for detection of probable clues to predict misleading information proliferated during COVID-19 outbreak.

In Neural computing & applications

Spreading of misleading information on social web platforms has fuelled huge panic and confusion among the public regarding the Corona disease, the detection of which is of paramount importance. To identify the credibility of the posted claim, we have analyzed possible evidence from the news articles in the google search results. This paper proposes an intelligent and expert strategy to gather important clues from the top 10 google search results related to the claim. The N-gram, Levenshtein Distance, and Word-Similarity-based features are used to identify the clues from the news article that can automatically warn users against spreading false news if no significant supportive clues are identified concerning that claim. The complete process is done in four steps, wherein the first step we build a query from the posted claim received in the form of text or text additive images which further goes as an input to the search query phase, where the top 10 google results are processed. In the third step, the important clues are extracted from titles of the top 10 news articles. Lastly, useful pieces of evidence are extracted from the content of each news article. All the useful clues with respect to N-gram, Levenshtein Distance, and Word Similarity are finally fed into the machine learning model for classification and to evaluate its performances. It has been observed that our proposed intelligent strategy gives promising experimental results and is quite effective in predicting misleading information. The proposed work provides practical implications for the policymakers and health practitioners that could be useful in protecting the world from misleading information proliferation during this pandemic.

Varshney Deepika, Vishwakarma Dinesh Kumar

2022-Nov-13

COVID-19, Fake news detection, Information pollution

General General

The COVID-19 Pandemic and Mental Health Concerns on Twitter in the United States.

In Health data science

Background : During the COVID-19 pandemic, mental health concerns (such as fear and loneliness) have been actively discussed on social media. We aim to examine mental health discussions on Twitter during the COVID-19 pandemic in the US and infer the demographic composition of Twitter users who had mental health concerns.

Methods : COVID-19-related tweets from March 5th, 2020, to January 31st, 2021, were collected through Twitter streaming API using keywords (i.e., "corona," "covid19," and "covid"). By further filtering using keywords (i.e., "depress," "failure," and "hopeless"), we extracted mental health-related tweets from the US. Topic modeling using the Latent Dirichlet Allocation model was conducted to monitor users' discussions surrounding mental health concerns. Deep learning algorithms were performed to infer the demographic composition of Twitter users who had mental health concerns during the pandemic.

Results : We observed a positive correlation between mental health concerns on Twitter and the COVID-19 pandemic in the US. Topic modeling showed that "stay-at-home," "death poll," and "politics and policy" were the most popular topics in COVID-19 mental health tweets. Among Twitter users who had mental health concerns during the pandemic, Males, White, and 30-49 age group people were more likely to express mental health concerns. In addition, Twitter users from the east and west coast had more mental health concerns.

Conclusions : The COVID-19 pandemic has a significant impact on mental health concerns on Twitter in the US. Certain groups of people (such as Males and White) were more likely to have mental health concerns during the COVID-19 pandemic.

Zhang Senqi, Sun Li, Zhang Daiwei, Li Pin, Liu Yue, Anand Ajay, Xie Zidian, Li Dongmei

2022

Public Health Public Health

COVID-19 classification using chest X-ray images based on fusion-assisted deep Bayesian optimization and Grad-CAM visualization.

In Frontiers in public health

The COVID-19 virus's rapid global spread has caused millions of illnesses and deaths. As a result, it has disastrous consequences for people's lives, public health, and the global economy. Clinical studies have revealed a link between the severity of COVID-19 cases and the amount of virus present in infected people's lungs. Imaging techniques such as computed tomography (CT) and chest x-rays can detect COVID-19 (CXR). Manual inspection of these images is a difficult process, so computerized techniques are widely used. Deep convolutional neural networks (DCNNs) are a type of machine learning that is frequently used in computer vision applications, particularly in medical imaging, to detect and classify infected regions. These techniques can assist medical personnel in the detection of patients with COVID-19. In this article, a Bayesian optimized DCNN and explainable AI-based framework is proposed for the classification of COVID-19 from the chest X-ray images. The proposed method starts with a multi-filter contrast enhancement technique that increases the visibility of the infected part. Two pre-trained deep models, namely, EfficientNet-B0 and MobileNet-V2, are fine-tuned according to the target classes and then trained by employing Bayesian optimization (BO). Through BO, hyperparameters have been selected instead of static initialization. Features are extracted from the trained model and fused using a slicing-based serial fusion approach. The fused features are classified using machine learning classifiers for the final classification. Moreover, visualization is performed using a Grad-CAM that highlights the infected part in the image. Three publically available COVID-19 datasets are used for the experimental process to obtain improved accuracies of 98.8, 97.9, and 99.4%, respectively.

Hamza Ameer, Attique Khan Muhammad, Wang Shui-Hua, Alhaisoni Majed, Alharbi Meshal, Hussein Hany S, Alshazly Hammam, Kim Ye Jin, Cha Jaehyuk

2022

Bayesian optimization, corona virus, deep learning, fusion, hyperparameters, multi-filters contrast enhancement

General General

How Does Misinformation and Capricious Opinions Impact the Supply Chain - A Study on the Impacts During the Pandemic.

In Annals of operations research

Misinformation or fake news has had multifaceted ramifications with the onset of the Covid-19 pandemic, creating widespread panic amongst people. This study investigates the impact of misinformation/ fake news (on internet platforms) on consumer buying behavior, impact of fear (created by fake news) on hoarding of essential products and consumer spending and finally impact of misinformation-induced panic buying on supply chain disruptions. It draws upon the consumer decision theory and the cognitive load theory for explaining the psychological and behavioral responses of consumers. The study follows an inductive approach towards theory building using a multi-method approach. Initially, a qualitative research method based on interviews followed by text-mining has been used followed by analysis using python for topic modelling using Latent Dirichlet Allocation (LDA). The findings revealed several prominent themes like consumer shift to online buying, two contrasting spending intentions namely financial security and compensatory consumptions, irrational panic buying, uncertainty/ambiguity of government protocol and norms, social media fraudulent practices and misinformation dissemination, personalized buying experience, reduced trust on news and marketers, logistics and transportation bottlenecks, labor shortage due to migration and plant closures, and bullwhip effect in supply chains.

Kar Arpan Kumar, Tripathi Shalini Nath, Malik Nishtha, Gupta Shivam, Sivarajah Uthayasankar

2022-Nov-07

Consumer buying behavior, Consumer spending, Fake news, Hoarding, Supply chain disruptions

General General

Were ride-hailing fares affected by the COVID-19 pandemic? Empirical analyses in Atlanta and Boston.

In Transportation

Ride-hailing services such as Lyft, Uber, and Cabify operate through smartphone apps and are a popular and growing mobility option in cities around the world. These companies can adjust their fares in real time using dynamic algorithms to balance the needs of drivers and riders, but it is still scarcely known how prices evolve at any given time. This research analyzes ride-hailing fares before and during the COVID-19 pandemic, focusing on applications of time series forecasting and machine learning models that may be useful for transport policy purposes. The Lyft Application Programming Interface was used to collect data on Lyft ride supply in Atlanta and Boston over 2 years (2019 and 2020). The Facebook Prophet model was used for long-term prediction to analyze the trends and global evolution of Lyft fares, while the Random Forest model was used for short-term prediction of ride-hailing fares. The results indicate that ride-hailing fares are affected during the COVID-19 pandemic, with values in the year 2020 being lower than those predicted by the models. The effects of fare peaks, uncontrollable events, and the impact of COVID-19 cases are also investigated. This study comes up with crucial policy recommendations for the ride-hailing market to better understand, regulate and integrate these services.

Silveira-Santos Tulio, González Ana Belén Rodríguez, Rangel Thais, Pozo Rubén Fernández, Vassallo Jose Manuel, Díaz Juan José Vinagre

2022-Nov-10

COVID-19, Dynamic Pricing, Machine Learning, Ride-Hailing, Time Series Forecasting, Transport Policy

General General

Face-mask-aware Facial Expression Recognition based on Face Parsing and Vision Transformer.

In Pattern recognition letters

As wearing face masks is becoming an embedded practice due to the COVID-19 pandemic, facial expression recognition (FER) that takes face masks into account is now a problem that needs to be solved. In this paper, we propose a face parsing and vision Transformer-based method to improve the accuracy of face-mask-aware FER. First, in order to improve the precision of distinguishing the unobstructed facial region as well as those parts of the face covered by a mask, we re-train a face-mask-aware face parsing model, based on the existing face parsing dataset automatically relabeled with a face mask and pixel label. Second, we propose a vision Transformer with a cross attention mechanism-based FER classifier, capable of taking both occluded and non-occluded facial regions into account and reweigh these two parts automatically to get the best facial expression recognition performance. The proposed method outperforms existing state-of-the-art face-mask-aware FER methods, as well as other occlusion-aware FER methods, on two datasets that contain three kinds of emotions (M-LFW-FER and M-KDDI-FER datasets) and two datasets that contain seven kinds of emotions (M-FER-2013 and M-CK+ datasets).

Yang Bo, Wu Jianming, Ikeda Kazushi, Hattori Gen, Sugano Masaru, Iwasawa Yusuke, Matsuo Yutaka

2022-Dec

41A05, 41A10, 65D05, 65D17, Covid-19, Deep learning, Face mask, Face parsing, Facial expression recognition, Vision transformer

Ophthalmology Ophthalmology

The Role of Technology in Ophthalmic Surgical Education During COVID-19.

In Current surgery reports

Purpose of Review : To describe the effect of COVID-19 on ophthalmic training programs and to review the various roles of technology in ophthalmology surgical education including virtual platforms, novel remote learning curricula, and the use of surgical simulators.

Recent Findings : COVID-19 caused significant disruption to in-person clinical and surgical patient encounters. Ophthalmology trainees worldwide faced surgical training challenges due to social distancing restrictions, trainee redeployment, and reduction in surgical case volume. Virtual platforms, such as Zoom and Microsoft Teams, were widely used during the pandemic to conduct remote teaching sessions. Novel virtual wet lab and dry lab curricula were developed. Training programs found utility in virtual reality surgical simulators, such as the Eyesi, to substitute experience lost from live patient surgical cases.

Summary : Although several of these described technologies were incorporated into ophthalmology surgical training programs prior to COVID-19, the pandemic highlighted the importance of developing a formal surgical curriculum that can be delivered virtually. Novel telementoring, collaboration between training institutions, and hybrid formats of didactic and practical training sessions should be continued. Future research should investigate the utility of augmented reality and artificial intelligence for trainee learning.

Hu Katherine S, Pettey Jeff, SooHoo Jeffrey R

2022-Nov-14

Ophthalmology training, Remote learning, Surgical simulators, Virtual education, Wet lab curriculum

General General

ETCNN: Extra Tree and Convolutional Neural Network-based Ensemble Model for COVID-19 Tweets Sentiment Classification.

In Pattern recognition letters

Pandemics influence people negatively and people experience fear and disappointment. With the global outspread of COVID-19, the sentiments of the general public are substantially influenced, and analyzing their sentiments could help to devise corresponding policies to alleviate negative sentiments. Often the data collected from social media platforms is unstructured leading to low classification accuracy. This study brings forward an ensemble model where the benefits of handcrafted features and automatic feature extraction are combined by machine learning and deep learning models. Unstructured data is obtained, preprocessed, and annotated using TextBlob and VADER before training machine learning models. Similarly, the efficiency of Word2Vec, TF, and TF-IDF features is also analyzed. Results reveal the better performance of the extra tree classifier when trained with TF-IDF features from TextBlob annotated data. Overall, machine learning models perform better with TF-IDF and TextBlob. The proposed model obtains superior performance using both annotation techniques with 0.97 and 0.95 scores of accuracy using TextBlob and VADER respectively with Word2Vec features. Results reveal that use of machine learning and deep learning models together with a voting criterion tends to yield better results than other machine learning models. Analysis of sentiments indicates that predominantly people possess negative sentiments regarding COVID-19.

Umer Muhammad, Sadiq Saima, Karamti Hanen, Abdulmajid Eshmawi Ala’, Nappi Michele, Usman Sana Muhammad, Ashraf Imran

2022-Dec

COVID-19, Ensemble model, Health informatics, Neuroinformatics, Sentiment analysis

General General

Fruit-CoV: An efficient vision-based framework for speedy detection and diagnosis of SARS-CoV-2 infections through recorded cough sounds.

In Expert systems with applications

COVID-19 is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This deadly virus has spread worldwide, leading to a global pandemic since March 2020. A recent variant of SARS-CoV-2 named Delta is intractably contagious and responsible for more than four million deaths globally. Therefore, developing an efficient self-testing service for SARS-CoV-2 at home is vital. In this study, a two-stage vision-based framework, namely Fruit-CoV, is introduced for detecting SARS-CoV-2 infections through recorded cough sounds. Specifically, audio signals are converted into Log-Mel spectrograms, and the EfficientNet-V2 network is used to extract their visual features in the first stage. In the second stage, 14 convolutional layers extracted from the large-scale Pretrained Audio Neural Networks for audio pattern recognition (PANNs) and the Wavegram-Log-Mel-CNN are employed to aggregate feature representations of the Log-Mel spectrograms and the waveform. Finally, the combined features are used to train a binary classifier. In this study, a dataset provided by the AICovidVN 115M Challenge is employed for evaluation. It includes 7,371 recorded cough sounds collected throughout Vietnam, India, and Switzerland. Experimental results indicate that the proposed model achieves an Area Under the Receiver Operating Characteristic Curve (AUC) score of 92.8% and ranks first on the final leaderboard of the AICovidVN 115M Challenge. Our code is publicly available.

Nguyen Long H, Pham Nhat Truong, Do Van Huong, Nguyen Liu Tai, Nguyen Thanh Tin, Nguyen Hai, Nguyen Ngoc Duy, Nguyen Thanh Thi, Nguyen Sy Dzung, Bhatti Asim, Lim Chee Peng

2023-Mar-01

COVID-19, Deep learning, Delta variant, EfficientNet, Log-Mel spectrogram, Machine vision, Neural network, PANNs, Recorded cough sounds, Remote detection, SARS-CoV-2 infections, Self-testing service, Sound classification, Speedy detection, Wavegram

General General

Cross-cultural factors influencing the adoption of virtual reality for practical learning.

In Universal access in the information society

Education is one area that was significantly affected by the COVID-19 pandemic with much of the education being transferred online. Many subjects that require hands-on experimental experience suffer when taught online. Education is also one area that many believe can benefit from the advances in virtual reality (VR) technology, particularly for remote, online learning. Furthermore, because the technology shows overall good results with hands-on experiential learning education, one possible way to overcome online education barriers is with the use of VR applications. Given that VR has yet to make significant inroads in education, it is essential to understand what factors will influence this technology's adoption and acceptance. In this work, we explore factors influencing the adoption of VR for hands-on practical learning around the world based on the Unified Theory of Acceptance and Use of Technology and three additional constructs. We also performed a cross-cultural analysis to examine the model fit for developed and developing countries and regions. Moreover, through open-ended questions, we gauge the overall feeling people in these countries have regarding VR for practical learning and how it compares with regular online learning.

Monteiro Diego, Ma Teng, Li Yue, Pan Zhigeng, Liang Hai-Ning

2022-Nov-15

COVID-19, Cross-cultural, Practical learning, Survey, Technology acceptance, Training, Virtual reality

General General

Is indoor and outdoor greenery associated with fewer depressive symptoms during COVID-19 lockdowns? A mechanistic study in Shanghai, China.

In Building and environment

Increasing numbers of studies have observed that indoor and outdoor greenery are associated with fewer depressive symptoms during COVID-19 lockdowns. However, most of these studies examined direct associations without sufficient attention to underlying pathways. Furthermore, few studies have combined different types of indoor and outdoor greenery to examine their effects on the alleviation of depressive symptoms. The present study hypothesized that indoor and outdoor exposure to greenery increased the perceived restorativeness of home environments, which, in turn, reduced loneliness, COVID-related fears, and, ultimately, depressive symptoms. To test our hypotheses, we conducted an online survey with 386 respondents in Shanghai, China, from April to May 2022, which corresponded to strict citywide lockdowns that resulted from the outbreak of the Omicron variant. Indoor greenery measures included the number of house plants, gardening activities, and digital nature exposure as well as semantic image segmentation applied to photographs from the most viewed windows to quantify indoor exposure to outdoor trees and grass. Outdoor greenery measures included total vegetative cover (normalized difference vegetation index [NDVI]) within a 300 m radius from the home and perceived quality of the community's greenery. Associations between greenery and depressive symptoms/clinical levels of depression, as measured by the Patient Health Questionnaire-9 (PHQ-9), were examined using generalized linear and logistic regression models. Structural equation modeling (SEM) was used to test pathways between greenery exposure, restorativeness, loneliness, fear of COVID-19, and depressive symptoms. The results showed that: 1) indoor and outdoor greenery were associated with fewer depressive symptoms; 2) greenery could increase the restorativeness of the home environment, which, in turn, was associated with fewer COVID-related mental stressors (i.e., loneliness and fear of COVID-19), and ultimately depressive symptoms; and 3) gender, education, and income did not modify associations between greenery and depressive symptoms. These findings are among the first to combine objective and subjective measures of greenery within and outside of the home and document their effects on mental health during lockdowns. Comprehensive enhancements of greenery in living environments could be nature-based solutions for mitigating COVID-19 related mental stressors.

Zhang Jinguang, Browning Matthew H E M, Liu Jie, Cheng Yingyi, Zhao Bing, Dadvand Payam

2023-Jan

Fear of COVID-19, Loneliness, Machine learning, Nature exposure, Visual access

General General

Time-delayed modelling of the COVID-19 dynamics with a convex incidence rate.

In Informatics in medicine unlocked

COVID-19 pandemic represents an unprecedented global health crisis which has an enormous impact on the world population and economy. Many scientists and researchers have combined efforts to develop an approach to tackle this crisis and as a result, researchers have developed several approaches for understanding the COVID-19 transmission dynamics and the way of mitigating its effect. The implementation of a mathematical model has proven helpful in further understanding the behaviour which has helped the policymaker in adopting the best policy necessary for reducing the spread. Most models are based on a system of equations which assume an instantaneous change in the transmission dynamics. However, it is believed that SARS-COV-2 have an incubation period before the tendency of transmission. Therefore, to capture the dynamics adequately, there would be a need for the inclusion of delay parameters which will account for the delay before an exposed individual could become infected. Hence, in this paper, we investigate the SEIR epidemic model with a convex incidence rate incorporated with a time delay. We first discussed the epidemic model as a form of a classical ordinary differential equation and then the inclusion of a delay to represent the period in which the susceptible and exposed individuals became infectious. Secondly, we identify the disease-free together with the endemic equilibrium state and examine their stability by adopting the delay differential equation stability theory. Thereafter, we carried out numerical simulations with suitable parameters choice to illustrate the theoretical result of the system and for a better understanding of the model dynamics. We also vary the length of the delay to illustrate the changes in the model as the delay parameters change which enables us to further gain an insight into the effect of the included delay in a dynamical system. The result confirms that the inclusion of delay destabilises the system and it forces the system to exhibit an oscillatory behaviour which leads to a periodic solution and it further helps us to gain more insight into the transmission dynamics of the disease and strategy to reduce the risk of infection.

Babasola Oluwatosin, Kayode Oshinubi, Peter Olumuyiwa James, Onwuegbuche Faithful Chiagoziem, Oguntolu Festus Abiodun

2022

34D20, 37N25, 39A60, 92B05, COVID-19, Convex incidence rate, Delay differential equation, SEIR epidemic model, Stability

Public Health Public Health

Emotion Analysis of COVID-19 Vaccines Based on a Fuzzy Convolutional Neural Network.

In Cognitive computation

COVID-19 created immense global challenges in 2020, and the world will live under its threat indefinitely. Much of the information on social media supported the government in addressing this major public health event. On January 9, to control the virus, the Chinese government announced universal vaccinations. However, due to a range of varied interpretations, people held different attitudes towards vaccination. Therefore, the success of the mass immunization strategy greatly depended on the public perception of the COVID-19 vaccine. This article explores the changes in people's emotional attitudes towards vaccines and the reasons behind them in the context of the global pandemic in an effort to help mankind overcome this ongoing crisis. For this article, microblogs from January to September containing Chinese people's responses to the COVID-19 vaccines were collected. Based on fuzzy logic and deep learning, we advance the hypothesis that fuzzy vector adaptive improvements will make it possible to better express language emotion and that fuzzy emotion vectors can be integrated into deep learning models, thus making these models more interpretable. Based on this assumption, we design a deep learning model with a fuzzy emotion vector. The experimental results show the positive effect of this model. By applying the model in analyses of people's attitudes towards vaccines, we can obtain people's attitudes towards vaccines in different time periods. We discovered that the most negative emotions about the vaccine appeared in April and that the most positive emotions about the vaccine appeared in February. Combined with word cloud technology and the LDA model, we can effectively explore the reasons for the changes in vaccine attitudes. Our findings show that people's negative emotions about the vaccine are always higher than their positive emotions about the vaccine and that people's attitudes towards the vaccine are closely related to the progress of the epidemic. There is also a certain relationship between people's attitudes towards the vaccine and those towards the vaccination.

Qiu Dong, Yu Yang, Chen Lei

2022-Nov-16

COVID-19 vaccines, Fuzzy convolutional neural network, Fuzzy emotion vector, Fuzzy logic, Sentiment analysis

General General

Enhanced Framework for COVID-19 Prediction with Computed Tomography Scan Images using Dense Convolutional Neural Network and Novel Loss Function.

In Computers & electrical engineering : an international journal

Recent studies have shown that computed tomography (CT) scan images can characterize COVID-19 disease in patients. Several deep learning (DL) methods have been proposed for diagnosis in the literature, including convolutional neural networks (CNN). But, with inefficient patient classification models, the number of 'False Negatives' can put lives at risk. The primary objective is to improve the model so that it does not reveal 'Covid' as 'Non-Covid'. This study uses Dense-CNN to categorize patients efficiently. A novel loss function based on cross-entropy has also been used to improve the CNN algorithm's convergence. The proposed model is built and tested on a recently published large dataset. Extensive study and comparison with well-known models reveal the effectiveness of the proposed method over known methods. The proposed model achieved a prediction accuracy of 93.78%, while false-negative is only 6.5%. This approach's significant advantage is accelerating the diagnosis and treatment of COVID-19.

Motwani Anand, Shukla Piyush Kumar, Pawar Mahesh, Kumar Manoj, Ghosh Uttam, Numay Waleed Al, Nayak Soumya Ranjan

2022-Nov-14

COVID-19, Chest CT-images, Classification, Deep Learning, Dense-Convolutional Neural Network, Loss function, Optimization, Prediction, SARS-CoV-2

General General

Comparative Evaluation of the Multilayer Perceptron Approach with Conventional ARIMA in Modeling and Prediction of COVID-19 Daily Death Cases.

In Journal of healthcare engineering

COVID-19 continues to pose a dangerous global health threat, as cases grow rapidly and deaths increase day by day. This increasing phenomenon does not only affect economic policy but also international policy around the world. In this paper, Pakistan daily death cases of COVID-19, from February 25, 2020, to March 23, 2022, have been modeled using the long-established autoregressive-integrated moving average (ARIMA) model and the machine learning multilayer perceptron (MLP) model. The most befitting model is selected based on the root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE). Values of the key performance indicator (KPI) showed that the MLP model outperformed the ARIMA model. The MLP model with 20 hidden layers, which emerged as the overall most apt model, was used to predict future daily COVID-19 deaths in Pakistan to enable policymakers and health professionals to put in place systematic measures to reduce death cases. We encourage the Government of Pakistan to intensify its vaccination campaign and encourage everyone to get vaccinated.

Qureshi Moiz, Daniyal Muhammad, Tawiah Kassim

2022

General General

Automatic social distance estimation for photographic studies: Performance evaluation, test benchmark, and algorithm.

In Machine learning with applications

The social distancing regulations introduced to slow down the spread of COVID-19 virus directly affect a basic form of non-verbal communication, and there may be longer term impacts on human behavior and culture that remain to be analyzed in proxemics studies. To obtain quantitative results for such studies, large media and/or personal photo collections must be analyzed. Several social distance monitoring methods have been proposed for safety purposes, but they are not directly applicable to general photo collections with large variations in the imaging setup. In such studies, the interest shifts from safety to analyzing subtle differences in social distances. Currently, there is no suitable benchmark for developing such algorithms. Collecting images with measured ground-truth pair-wise distances using different camera settings is cumbersome. Moreover, performance evaluation for these algorithms is not straightforward, and there is no widely accepted evaluation protocol. In this paper, we provide an image dataset with measured pair-wise social distances under different camera positions and settings. We suggest a performance evaluation protocol and provide a benchmark to easily evaluate such algorithms. We also propose an automatic social distance estimation method that can be applied on general photo collections. Our method is a hybrid method that combines deep learning-based object detection and human pose estimation with projective geometry. The method can be applied on uncalibrated single images with known focal length and sensor size. The results on our benchmark are encouraging with 91% human detection rate and only 38.24% average relative distance estimation error among the detected people.

Seker Mert, Männistö Anssi, Iosifidis Alexandros, Raitoharju Jenni

2022-Nov-09

Human Pose Estimation, Performance Evaluation, Person detection, Proxemics, Social distance estimation, Test Benchmark

General General

An automated multi-web platform voting framework to predict misleading information proliferated during COVID-19 outbreak using ensemble method.

In Data & knowledge engineering

The spreading of misleading information on social web platforms has fuelled massive panic and confusion among the public regarding the Corona disease, the detection of which is of paramount importance. Previous studies mainly relied on a specific web platform to collect crucial evidence to detect fake content. The analysis identifies that retrieving clues from two or more different sources/web platforms gives more reliable prediction and confidence concerning a specific claim. This study proposed a novel multi-web platform voting framework that incorporates 4 sets of novel features: content, linguistic, similarity, and sentiments. The features have been gathered from each web-platforms to validate the news. To validate the fact/claim, a unique source platform is designed to collect relevant clues/headlines from two web platforms (YouTube, Google) based on specific queries and extracted features concerning each clue/headline. The proposed idea is to incorporate a unique platform to assist researchers in gathering relevant and vital evidence from diverse web platforms. After evaluation and validation, it has been identified that the built model is quite intelligent, gives promising results, and effectively predicts misleading information. The model correctly detected about 98% of the COVID misinformation on the constraint Covid-19 fake news dataset. Furthermore, it is observed that it is efficient to gather clues from multiple web platforms for more reliable predictions to validate the news. The suggested work depicts numerous practical applications for health policy-makers and practitioners that could be useful in safeguarding and implicating awareness among society from misleading information dissemination during this pandemic.

Varshney Deepika, Vishwakarma Dinesh Kumar

2022-Nov-11

COVID-19, Fake news, Google, Machine learning, Misleading information, Multi-web platforms, YouTube

General General

Bridging the Gap between Target-Based and Cell-Based Drug Discovery with a Graph Generative Multitask Model.

In Journal of chemical information and modeling

The development of new drugs is crucial for protecting humans from disease. In the past several decades, target-based screening has been one of the most popular methods for developing new drugs. This method efficiently screens potential inhibitors of a target protein in vitro, but it frequently fails in vivo due to insufficient activity of the selected drugs. There is a need for accurate computational methods to bridge this gap. Here, we present a novel graph multi-task deep learning model to identify compounds with both target inhibitory and cell active (MATIC) properties. On a carefully curated SARS-CoV-2 data set, the proposed MATIC model shows advantages compared with the traditional method in screening effective compounds in vivo. Following this, we investigated the interpretability of the model and discovered that the learned features for target inhibition (in vitro) or cell active (in vivo) tasks are different with molecular property correlations and atom functional attention. Based on these findings, we utilized a Monte Carlo-based reinforcement learning generative model to generate novel multiproperty compounds with both in vitro and in vivo efficacy, thus bridging the gap between target-based and cell-based drug discovery. The tool is freely accessible at https://github.com/SIAT-code/MATIC.

Hu Fan, Wang Dongqi, Huang Huazhen, Hu Yishen, Yin Peng

2022-Nov-19

oncology Oncology

Assessment of Oncology Patient Engagement and Interest in Virtual Mind-Body Programming: Moving Toward Personalization of Virtual Care.

In JCO oncology practice

PURPOSE : Accelerated by the COVID-19 pandemic, the virtual platform has become a prominent medium to deliver mind-body therapies, but the extent to which patients engage in virtual mind-body programming remains unclear. This study aims to assess oncology patient engagement in a virtual mind-body program.

METHODS : We surveyed oncology patients enrolled in a live-streamed (synchronous) virtual mind-body program in May 2021. Patients self-reported engagement by weekly attendance. We applied multivariate regression to identify associations of engagement with sociodemographic and clinical factors. As an exploratory analysis, we used machine learning to partition engagement subgroups to determine preferential interest in prerecorded (asynchronous) mind-body therapy videos.

RESULTS : Among 148 patients surveyed (response rate: 21.4%), majority were female (94.5%), White (83.1%), age 65 years or older (64.9%), retired (64.2%), and in survivorship (61.8%). Patient engagement ranged from 1 to 13 classes/week (mean [standard deviation]: 4.23 [2.56]) and was higher for female (β, .82; 95% CI, 0.01 to 1.62), non-White (β, .63; 95% CI, 0.13 to 1.13), and retired patients (β, .50; 95% CI, 0.12 to 0.88). The partition model identified three engagement subgroups: employed (low engagers), retired White (intermediate engagers), and retired non-White (high engagers). Particularly, low engagers had preferential interest in meditation videos (odds ratio, 2.85; 95% CI, 1.24 to 6.54), and both low and high engagers had preferential interest in Tai Chi videos (odds ratio, 2.26; 95% CI, 1.06 to 4.82).

CONCLUSION : In this cross-sectional study among oncology patients, engagement in virtual mind-body programming was higher for female, non-White, and retired patients. Our findings suggest the need for both synchronous and asynchronous mind-body programming to meet the diverse needs of oncology patients.

Hung Tony K W, Latte-Naor Shelly, Li Yuelin, Kuperman Gilad J, Seluzicki Christina, Pendleton Eva, Pfister David G, Mao Jun J

2022-Nov-18

General General

Artificial intelligence-based analytics for impacts of COVID-19 and online learning on college students' mental health.

In PloS one ; h5-index 176.0

COVID-19, the disease caused by the novel coronavirus (SARS-CoV-2), first emerged in Wuhan, China late in December 2019. Not long after, the virus spread worldwide and was declared a pandemic by the World Health Organization in March 2020. This caused many changes around the world and in the United States, including an educational shift towards online learning. In this paper, we seek to understand how the COVID-19 pandemic and the increase in online learning impact college students' emotional wellbeing. We use several machine learning and statistical models to analyze data collected by the Faculty of Public Administration at the University of Ljubljana, Slovenia in conjunction with an international consortium of universities, other higher education institutions, and students' associations. Our results indicate that features related to students' academic life have the largest impact on their emotional wellbeing. Other important factors include students' satisfaction with their university's and government's handling of the pandemic as well as students' financial security.

Rezapour Mostafa, Elmshaeuser Scott K

2022

Radiology Radiology

Utilizing an organizational development framework as a road map for creating a technology-driven agile curriculum in predoctoral dental education.

In Journal of dental education

The landscape of dental education is undergoing a paradigm shift from both the learner's and teacher's perspectives. Evolving technologies, including artificial intelligence, virtual reality, augmented reality, and mixed reality, are providing synergistic opportunities to create new and exciting educational platforms. The evolution of these platforms will likely play a significant role in dental education. This is especially true in the wake of calamities like the COVID-19 pandemic during which educational activities had to be shutdown or moved online. This experience demonstrated that it is prudent to develop curricula that are both agile and efficient via creating hybrid courses that provide effective learning experiences regardless of the mode of delivery. Although there is growing interest in incorporating technology into dental education, there are few examples of how to actually manage the implementation of technology into the curriculum. In this paper, we provide a road map for incorporating technology into the dental curriculum to create agility and discuss challenges and possible solutions.

Tadinada Aditya, Gul Gulsun, Godwin Lauren, Al Sakka Yacoub, Crain Geralyn, Stanford Clark M, Johnson Jeffrey

2022-Nov-18

computer-assisted instruction, curriculum innovation, dental education, institutional/organizational development, patient simulation

General General

Perceived usefulness of COVID-19 tools for contact tracing among contact tracers in Korea.

In Epidemiology and health

Objectives : In Korea, contact tracing for coronavirus disease 2019 is conducted using the information from credit card records, handwritten visitor logs, KI-Pass (QR code), and Safe Call after an interview. We aimed to assess the usefulness of these tools for contact tracing.

Methods : The 2 months (July to September 2021) long anonymous online survey was conducted. Contact tracers from throughout Korea were included as the participants. The questionnaire consisted of 4 parts: 1) demographic characteristics, 2) usefulness of each tool for contact tracing, 3) order in which information is checked during contact tracing, and 4) match rate between tools for contact tracing, screening test rate, response rate, and helpfulness (rated on a Likert scale).

Results : A total of 190 individuals participated in the survey. When asked to rate the usefulness of each tool for contact tracing on a Likert scale, most respondents (86%) provided positive response for "credit card records", while the most common response for "handwritten visitor logs" was negative. The actual helpfulness of positive response was KI-Pass (91%), Credit card records (83%), Safe Call (78%), and Handwritten visitor logs (22%).

Conclusion : Over 80% of participants provided positive responses to credit card records, KI-Pass, and Safe Call data, while approximately 50% provided negative responses regarding the usefulness of handwritten visitor logs. Our findings highlight the need to unify systems for contact tracing performed after an interview to increase their convenience for contact tracers, as well as the need to improve tools that utilize handwritten visitor logs for digitally vulnerable groups.

Gong Seonyeong, Moon Jong Youn, Jung Jaehun

2022-Nov-15

COVID-19, Contact tracing, Entry log, KI-Pass

General General

Cell-type annotation with accurate unseen cell-type identification using multiple references

bioRxiv Preprint

Automated cell-type annotation using a well-annotated single-cell RNA-sequencing (scRNA-seq) reference relies on the diversity of cell types in the reference. However, for technical and biological reasons, new query data of interest may contain unseen cell types that are missing from the reference. When annotating new query data, identifying the unseen cell type is fundamental not only to improve annotation accuracy but also to new biological discoveries. Here, we propose mtANN (multiple-reference-based scRNA-seq data annotation), a new method to automatically annotate query data while accurately identifying unseen cell types with the help of multiple references. Key innovations of mtANN include the integration of deep learning and ensemble learning to improve prediction accuracy, and the introduction of a new metric defined from three complementary aspects to identify unseen cell types. We demonstrate the advantages of mtANN over state-of-the-art methods for cell-type annotation and unseen cell-type identification on two benchmark dataset collections, as well as its predictive power on a collection of COVID-19 datasets.

Yixuan, X.; Mengguo, W.; Luonan, C.; Xiaofei, Z.

2022-11-18

General General

Persistent Laplacian projected Omicron BA.4 and BA.5 to become new dominating variants.

In Computers in biology and medicine

Due to its high transmissibility, Omicron BA.1 ousted the Delta variant to become a dominating variant in late 2021 and was replaced by more transmissible Omicron BA.2 in March 2022. An important question is which new variants will dominate in the future. Topology-based deep learning models have had tremendous success in forecasting emerging variants in the past. However, topology is insensitive to homotopic shape evolution in virus-human protein-protein binding, which is crucial to viral evolution and transmission. This challenge is tackled with persistent Laplacian, which is able to capture both the topological change and homotopic shape evolution of data. Persistent Laplacian-based deep learning models are developed to systematically evaluate variant infectivity. Our comparative analysis of Alpha, Beta, Gamma, Delta, Lambda, Mu, and Omicron BA.1, BA.1.1, BA.2, BA.2.11, BA.2.12.1, BA.3, BA.4, and BA.5 unveils that Omicron BA.2.11, BA.2.12.1, BA.3, BA.4, and BA.5 are more contagious than BA.2. In particular, BA.4 and BA.5 are about 36% more infectious than BA.2 and are projected to become new dominant variants by natural selection. Moreover, the proposed models outperform the state-of-the-art methods on three major benchmark datasets for mutation-induced protein-protein binding free energy changes. Our key projection about BA4 and BA.5's dominance made on May 1, 2022 (see arXiv:2205.00532) became a reality in late June 2022.

Chen Jiahui, Qiu Yuchi, Wang Rui, Wei Guo-Wei

2022-Nov-02

Deep learning, Evolution, Infectivity, Persistent Laplacian, SARS-CoV-2

General General

Recommendations for Successful Implementation of the Use of Vocal Biomarkers for Remote Monitoring of COVID-19 and Long COVID in Clinical Practice and Research.

In Interactive journal of medical research

The COVID-19 pandemic accelerated the use of remote patient monitoring in clinical practice or research for safety and emergency reasons, justifying the need for innovative digital health solutions to monitor key parameters or symptoms related to COVID-19 or Long COVID. The use of voice-based technologies, and in particular vocal biomarkers, is a promising approach, voice being a rich, easy-to-collect medium with numerous potential applications for health care, from diagnosis to monitoring. In this viewpoint, we provide an overview of the potential benefits and limitations of using voice to monitor COVID-19, Long COVID, and related symptoms. We then describe an optimal pipeline to bring a vocal biomarker candidate from research to clinical practice and discuss recommendations to achieve such a clinical implementation successfully.

Fischer Aurelie, Elbeji Abir, Aguayo Gloria, Fagherazzi Guy

2022-Nov-15

COVID-19, COVID-19 symptoms, Long COVID, artificial intelligence, digital health, digital health monitoring, digital health solution, health care application, health monitoring, health technology, remote monitoring, remote patient monitoring, vocal biomarker, voice, voice-based technology

Radiology Radiology

Cardiovascular CT, MRI, and PET/CT in 2021: Review of Key Articles.

In Radiology ; h5-index 91.0

This review focuses on three key noninvasive cardiac imaging modalities-cardiac CT angiography (CTA), MRI, and PET/CT-and summarizes key publications in 2021 relevant to radiologists in clinical practice. Although this review focuses primarily on articles published in Radiology, important studies from other major journals are included to highlight "must-know" articles in the field of cardiovascular imaging. Cardiac CTA has been established as the first-line test for patients with stable chest pain and no known coronary artery disease, and its value remains central to the assessment of surgical or transcatheter aortic valve replacement. Artificial intelligence continues to evolve in a number of applications in cardiovascular disease. In cardiac MRI studies, 2021 has seen an emphasis on nonischemic cardiomyopathies, valvular heart disease, and COVID-19 disease cardiac manifestations and the authors highlight the key articles on these topics. A section featuring the increasing role of cardiac PET/CT in the assessment of cardiac sarcoidosis and prosthetic valves is also provided.

Tzimas Georgios, Ryan David T, Murphy David J, Leipsic Jonathon A, Dodd Jonathan D

2022-Nov-15

General General

Symptom Clusters Seen in Adult COVID-19 Recovery Clinic Care Seekers.

In Journal of general internal medicine ; h5-index 57.0

BACKGROUND : COVID-19 symptom reports describe varying levels of disease severity with differing periods of recovery and symptom trajectories. Thus, there are a multitude of disease and symptom characteristics clinicians must navigate and interpret to guide care.

OBJECTIVE : To find natural groups of patients with similar constellations of post-acute sequelae of COVID-19 (PASC) symptoms.

DESIGN : Cohort SETTING: Outpatient COVID-19 recovery clinic with patient referrals from 160 primary care clinics serving 36 counties in Texas.

PATIENTS : Adult patients seeking COVID-19 recovery clinic care between November 15, 2020, and July 31, 2021, with laboratory-confirmed mild (not hospitalized), moderate (hospitalized), or severe (hospitalized with critical care) COVID-19.

MAIN MEASURES : Demographics, COVID illness onset, and duration of persistent PASC symptoms via semi-structured medical assessments.

KEY RESULTS : Four hundred forty-one patients (mean age 51.5 years; 295 [66.9%] women; 99 [22%] Hispanic, and 170 [38.5%] non-White, racial minority) met inclusion criteria. Using a k-medoids algorithm, we found that PASC symptoms cluster into two distinct groups: neuropsychiatric (N = 186) (e.g., subjective cognitive dysfunction) and pulmonary (N = 255) (e.g., dyspnea, cough). The neuropsychiatric cluster had significantly higher incidences of otolaryngologic (X2 = 14.3, p < 0.001), gastrointestinal (X2 = 6.90, p = 0.009), neurologic (X2 = 441, p < 0.001), and psychiatric sequelae (X2 = 40.6, p < 0.001) with more female (X2 = 5.44, p = 0.020) and younger age (t = 2.39, p = 0.017) patients experiencing longer durations of PASC symptoms before seeking care (t = 2.44, p = 0.015). Patients in the pulmonary cluster were more often hospitalized for COVID-19 (X2 = 3.98, p = 0.046) and had significantly higher comorbidity burden (U = 20800, p = 0.019) and pulmonary sequelae (X2 = 13.2, p < 0.001).

CONCLUSIONS : Health services clinic data from a large integrated health system offers insights into the post-COVID symptoms associated with care seeking for sequelae that are not adequately managed by usual care pathways (self-management and primary care clinic visits). These findings can inform machine learning algorithms, primary care management, and selection of patients for earlier COVID-19 recovery referral.

TRIAL REGISTRATION : N/A.

Danesh Valerie, Arroliga Alejandro C, Bourgeois James A, Boehm Leanne M, McNeal Michael J, Widmer Andrew J, McNeal Tresa M, Kesler Shelli R

2022-Nov-14

General General

A Machine Learning Approach to Identify Predictors of Severe COVID-19 Outcome in Patients With Rheumatoid Arthritis.

In Pain physician ; h5-index 45.0

BACKGROUND : Rheumatoid arthritis (RA) patients have a lowered immune response to infection, potentially due to the use of corticosteroids and immunosuppressive drugs. Predictors of severe COVID-19 outcomes within the RA population have not yet been explored in a real-world setting.

OBJECTIVES : To identify the most influential predictors of severe COVID-19 within the RA population.

STUDY DESIGN : Retrospective cohort study.

SETTING : Research was conducted using Optum's de-identified Clinformatics® Data Mart Database (2000-2021Q1), a US commercial claims database.

METHODS : We identified adult patients with index COVID-19 (ICD-10-CM diagnosis code U07.1) between March 1, 2020, and December 31, 2020. Patients were required to have continuous enrollment and have evidence of one inpatient or 2 outpatient diagnoses of RA in the 365 days prior to index. RA patients with COVID-19 were stratified by outcome (mild vs severe), with severe cases defined as having one of the following within 60 days of COVID-19 diagnosis: death, treatment in the intensive care unit (ICU), or mechanical ventilation. Baseline demographics and clinical characteristics were extracted during the 365 days prior to index COVID-19 diagnosis. To control for improving treatment options, the month of index date was included as a potential independent variable in all models. Data were partitioned (80% train and 20% test), and a variety of machine learning algorithms (logistic regression, random forest, support vector machine [SVM], and XGBoost) were constructed to predict severe COVID-19, with model covariates ranked according to importance.

RESULTS : Of 4,295 RA patients with COVID-19 included in the study, 990 (23.1%) were classified as severe. RA patients with severe COVID-19 had a higher mean age (mean [SD] = 71.6 [10.3] vs 63.4 [13.7] years, P < 0.001) and Charlson Comorbidity Index (CCI) (3.8 [2.4] vs 2.4 [1.8], P < 0.001) than those with mild cases. Males were more likely to be a severe case than mild (29.1% vs 18.5%, P < 0.001). The top 15 predictors from the best performing model (XGBoost, AUC = 75.64) were identified. While female gender, commercial insurance, and physical therapy were inversely associated with severe COVID-19 outcomes, top predictors included a March index date, older age, more inpatient visits at baseline, corticosteroid or gamma-aminobutyric acid analog (GABA) use at baseline or the need for durable medical equipment (i.e., wheelchairs), as well as comorbidities such as congestive heart failure, hypertension, fluid and electrolyte disorders, lower respiratory disease, chronic pulmonary disease, and diabetes with complication.

LIMITATIONS : The cohort meeting our eligibility criteria is a relatively small sample in the context of machine learning. Additionally, diagnoses definitions rely solely on ICD-10-CM codes, and there may be unmeasured variables (such as labs and vitals) due to the nature of the data. These limitations were carefully considered when interpreting the results.

CONCLUSIONS : Predictive baseline comorbidities and risk factors can be leveraged for early detection of RA patients at risk of severe COVID-19 outcomes. Further research should be conducted on modifiable factors in the RA population, such as physical therapy.

Burns Sara M, Woodworth TIffany S, Icten Zeynep, Honda Trenton, Manjourides Justin

2022-Nov

** RA, SARS-CoV-2, corticosteroid use\r, machine learning, physical therapy, predictive modeling, real-world data, real-world evidence, rheumatoid arthritis, COVID-19**

General General

CNN Features and Optimized Generative Adversarial Network for COVID-19 Detection from Chest X-Ray Images.

In Critical reviews in biomedical engineering

Coronavirus is a RNA type virus, which makes various respiratory infections in both human as well as animals. In addition, it could cause pneumonia in humans. The Coronavirus affected patients has been increasing day to day, due to the wide spread of diseases. As the count of corona affected patients increases, most of the regions are facing the issue of test kit shortage. In order to resolve this issue, the deep learning approach provides a better solution for automatically detecting the COVID-19 disease. In this research, an optimized deep learning approach, named Henry gas water wave optimization-based deep generative adversarial network (HGWWO-Deep GAN) is developed. Here, the HGWWO algorithm is designed by the hybridization of Henry gas solubility optimization (HGSO) and water wave optimization (WWO) algorithm. The pre-processing method is carried out using region of interest (RoI) and median filtering in order to remove the noise from the images. Lung lobe segmentation is carried out using U-net architecture and lung region extraction is done using convolutional neural network (CNN) features. Moreover, the COVID-19 detection is done using Deep GAN trained by the HGWWO algorithm. The experimental result demonstrates that the developed model attained the optimal performance based on the testing accuracy of 0.9169, sensitivity of 0.9328, and specificity of 0.9032.

Kalpana Gotlur, Durga A Kanaka, Karuna G

2022

General General

Interpretable tourism volume forecasting with multivariate time series under the impact of COVID-19.

In Neural computing & applications

This study proposes a novel interpretable framework to forecast the daily tourism volume of Jiuzhaigou Valley, Huangshan Mountain, and Siguniang Mountain in China under the impact of COVID-19 by using multivariate time-series data, particularly historical tourism volume data, COVID-19 data, the Baidu index, and weather data. For the first time, epidemic-related search engine data is introduced for tourism demand forecasting. A new method named the composition leading search index-variational mode decomposition is proposed to process search engine data. Meanwhile, to overcome the problem of insufficient interpretability of existing tourism demand forecasting, a new model of DE-TFT interpretable tourism demand forecasting is proposed in this study, in which the hyperparameters of temporal fusion transformers (TFT) are optimized intelligently and efficiently based on the differential evolution algorithm. TFT is an attention-based deep learning model that combines high-performance forecasting with interpretable analysis of temporal dynamics, displaying excellent performance in forecasting research. The TFT model produces an interpretable tourism demand forecast output, including the importance ranking of different input variables and attention analysis at different time steps. Besides, the validity of the proposed forecasting framework is verified based on three cases. Interpretable experimental results show that the epidemic-related search engine data can well reflect the concerns of tourists about tourism during the COVID-19 epidemic.

Wu Binrong, Wang Lin, Tao Rui, Zeng Yu-Rong

2022-Nov-04

COVID-19, Deep learning, Interpretable tourism demand forecasting, Variational mode decomposition

General General

A survey on deep learning applied to medical images: from simple artificial neural networks to generative models.

In Neural computing & applications

Deep learning techniques, in particular generative models, have taken on great importance in medical image analysis. This paper surveys fundamental deep learning concepts related to medical image generation. It provides concise overviews of studies which use some of the latest state-of-the-art models from last years applied to medical images of different injured body areas or organs that have a disease associated with (e.g., brain tumor and COVID-19 lungs pneumonia). The motivation for this study is to offer a comprehensive overview of artificial neural networks (NNs) and deep generative models in medical imaging, so more groups and authors that are not familiar with deep learning take into consideration its use in medicine works. We review the use of generative models, such as generative adversarial networks and variational autoencoders, as techniques to achieve semantic segmentation, data augmentation, and better classification algorithms, among other purposes. In addition, a collection of widely used public medical datasets containing magnetic resonance (MR) images, computed tomography (CT) scans, and common pictures is presented. Finally, we feature a summary of the current state of generative models in medical image including key features, current challenges, and future research paths.

Celard P, Iglesias E L, Sorribes-Fdez J M, Romero R, Vieira A Seara, Borrajo L

2022-Nov-04

Artificial neural networks, Computer vision, Convolutional neural networks, Generative adversarial networks, Medical imaging, Variational autoencoders

Public Health Public Health

Practice of big data and artificial intelligence in epidemic surveillance and containment.

In Intelligent medicine

Faced with the current time-sensitive COVID-19 pandemic, the overburdened healthcare systems resulted in a strong demand to develop newer methods to control the spread of the pandemic. Big data and artificial intelligence (AI) have been leveraged amid the COVID-19 pandemic; however, little is known about its use for supporting public health efforts. In epidemic surveillance and containment, efforts are needed to treat critical patients, track and manage the health status of residents, isolate suspected cases, develop vaccines and antiviral drugs. The applications of emerging practices of artificial intelligence and big data have become powerful "weapons" to fight against the pandemic and provide strong support in pandemic prevention and control, such as early warning, analysis and judgment, interruption and intervention of epidemic, to achieve goals of early detection, early report, early diagnosis, early isolation and early treatment, and these are the decisive factors to control the spread of the epidemic and reduce the mortality. This paper systematically summarizes the application of big data and AI in epidemic, and describes practical cases and challenges with emphasis in epidemic prevention and control. The included studies showed that big data and AI have the potential strength to fight against COVID-19. However, many of the proposed methods are not yet widely accepted. Thus, the most rewarding research will be on methods promising value beyond COVID-19. More efforts are needed for developing standardized reporting protocols or guidelines for practice.

Jiao Zengtao, Ji Hanran, Yan Jun, Qi Xiaopeng

2022-Nov-05

Artificial intelligence, Big data, Early warning, Epidemic prevention and control, Epidemiological investigation

General General

Designing Resilient Manufacturing Systems using Cross Domain Application of Machine Learning Resilience.

In Procedia CIRP

The COVID-19 pandemic and crises like the Ukraine-Russia war have led to numerous restrictions for industrial manufacturing due to interrupted supply chains, staff absences due to illness or quarantine measures, and order situations that changed significantly at short notice. These influences have exposed that it is crucial to address the issue of manufacturing resilience in the context of current disruptions. This can be plausibly guaranteed by subjecting the ML model of a manufacturing system to attacks deliberately designed to fool its prediction. Such attacks can provide useful insights into properties that can increase resilience of manufacturing systems.

Mukherjee Avik, Glatt Moritz, Mustafa Waleed, Kloft Marius, Aurich Jan C

2022

adverserial attacks, adverserial training, deep neural networks, discrete-event simulation environment, machine learning, manufacturing system, resilience, supply network

General General

Estimating Discontinuous Time-Varying Risk Factors and Treatment Benefits for COVID-19 with Interpretable ML

ArXiv Preprint

Treatment protocols, disease understanding, and viral characteristics changed over the course of the COVID-19 pandemic; as a result, the risks associated with patient comorbidities and biomarkers also changed. We add to the conversation regarding inflammation, hemostasis and vascular function in COVID-19 by performing a time-varying observational analysis of over 4000 patients hospitalized for COVID-19 in a New York City hospital system from March 2020 to August 2021. To perform this analysis, we apply tree-based generalized additive models with temporal interactions which recover discontinuous risk changes caused by discrete protocols changes. We find that the biomarkers of thrombosis increasingly predicted mortality from March 2020 to August 2021, while the association between biomarkers of inflammation and thrombosis weakened. Beyond COVID-19, this presents a straightforward methodology to estimate unknown and discontinuous time-varying effects.

Benjamin Lengerich, Mark E. Nunnally, Yin Aphinyanaphongs, Rich Caruana

2022-11-15

General General

CXR-Net: A Multitask Deep Learning Network for Explainable and Accurate Diagnosis of COVID-19 Pneumonia from Chest X-ray Images.

In IEEE journal of biomedical and health informatics

Accurate and rapid detection of COVID-19 pneumonia is crucial for optimal patient treatment. Chest X-Ray (CXR) is the first-line imaging technique for COVID-19 pneumonia diagnosis as it is fast, cheap and easily accessible. Currently, many deep learning (DL) models have been proposed to detect COVID-19 pneumonia from CXR images. Unfortunately, these deep classifiers lack the transparency in interpreting findings, which may limit their applications in clinical practice. The existing explanation methods produce either too noisy or imprecise results, and hence are unsuitable for diagnostic purposes. In this work, we propose a novel explainable CXR deep neural Network (CXR-Net) for accurate COVID-19 pneumonia detection with an enhanced pixel-level visual explanation using CXR images. An Encoder-Decoder-Encoder architecture is proposed, in which an extra encoder is added after the encoder-decoder structure to ensure the model can be trained on category samples. The method has been evaluated on real world CXR datasets from both public and private sources, including healthy, bacterial pneumonia, viral pneumonia and COVID-19 pneumonia cases. The results demonstrate that the proposed method can achieve a satisfactory accuracy and provide fine-resolution activation maps for visual explanation in the lung disease detection. The Average Accuracy, Sensitivity, Specificity, PPV and F1-score of models in the COVID-19 pneumonia detection reach 0.992, 0.998, 0.985 and 0.989, respectively. Compared to current state-of-the-art visual explanation methods, the proposed method can provide more detailed, high-resolution, visual explanation for the classification results. It can be deployed in various computing environments, including cloud, CPU and GPU environments. It has a great potential to be used in clinical practice for COVID-19 pneumonia diagnosis.

Zhang Xin, Han Liangxiu, Sobeih Tam, Han Lianghao, Dempsey Nina, Lechareas Symeon, Tridente Ascanio, Chen Haoming, White Stephen, Zhang Daoqiang

2022-Nov-09

General General

LitCovid in 2022: an information resource for the COVID-19 literature.

In Nucleic acids research ; h5-index 217.0

LitCovid (https://www.ncbi.nlm.nih.gov/research/coronavirus/)-first launched in February 2020-is a first-of-its-kind literature hub for tracking up-to-date published research on COVID-19. The number of articles in LitCovid has increased from 55 000 to ∼300 000 over the past 2.5 years, with a consistent growth rate of ∼10 000 articles per month. In addition to the rapid literature growth, the COVID-19 pandemic has evolved dramatically. For instance, the Omicron variant has now accounted for over 98% of new infections in the United States. In response to the continuing evolution of the COVID-19 pandemic, this article describes significant updates to LitCovid over the last 2 years. First, we introduced the long Covid collection consisting of the articles on COVID-19 survivors experiencing ongoing multisystemic symptoms, including respiratory issues, cardiovascular disease, cognitive impairment, and profound fatigue. Second, we provided new annotations on the latest COVID-19 strains and vaccines mentioned in the literature. Third, we improved several existing features with more accurate machine learning algorithms for annotating topics and classifying articles relevant to COVID-19. LitCovid has been widely used with millions of accesses by users worldwide on various information needs and continues to play a critical role in collecting, curating and standardizing the latest knowledge on the COVID-19 literature.

Chen Qingyu, Allot Alexis, Leaman Robert, Wei Chih-Hsuan, Aghaarabi Elaheh, Guerrerio John J, Xu Lilly, Lu Zhiyong

2022-Nov-09

General General

Predicting factors affecting the intention to use a 3PL during the COVID-19 pandemic: A machine learning ensemble approach.

In Heliyon

The COVID-19 pandemic had brought changes to individuals, especially in consumer behavior. As the government of different countries has been implementing safety protocols to mitigate the spread of the virus, people became apprehensive about traveling and going out. This paved way for the emergence of third-party logistics (3PL). Statistics have proven the rapid escalation regarding the use of 3PL in various countries. This study utilized Artificial Neural Network and Random Forest Classifier to validate and justify the factors that affect consumer intention in selecting a 3PL service provider during the COVID-19 pandemic integrating the Service Quality Dimensions and Pro-Environmental Theory of Planned Behavior. The findings of this study revealed that attitude is the most significant factor that affects the consumers' behavioral intention. Other factors such as customer satisfaction, customer perceived value, perceived environmental concern, assurance, responsiveness, empathy, reliability, tangibility, perceived behavioral control, subjective norm, and perceived authority support, are all contributing factors that affect behavioral intention. Machine learning algorithms, specifically ANN and RFC, resulted to be reliable in predicting factors as they obtained accuracy rates of 98.56% and 93%. Results presented that consumers' attitude, satisfaction, perceived value, assurance by the 3PL, and perceived environmental concerns were highly influential in choosing a 3PL package carrier. It was seen that people would be encouraged to use 3PL service providers if they demonstrate availability and environmental concerns in catering to the customers' needs. Subsequently, 3PL providers must assure safety and convenience before, during, and after providing the service to ensure continuous patronage of consumers. This is considered to be the first study that utilized a machine learning ensemble to measure behavioral intention for the logistic sector. The framework, analysis tools, and findings of this study could be extended and applied among other behavioral intentions regarding transportation worldwide. Managerial insights among service providers are discussed.

German Josephine D, Ong Ardvin Kester S, Perwira Redi Anak Agung Ngurah, Robas Kirstien Paola E

2022-Nov

Artificial neural network, Behavioral intention, Random forest classifier, Third party logistics

Public Health Public Health

COVID-19 Health Beliefs Regarding Mask Wearing and Vaccinations on Twitter: Deep Learning Approach.

In JMIR infodemiology

Background : Amid the global COVID-19 pandemic, a worldwide infodemic also emerged with large amounts of COVID-19-related information and misinformation spreading through social media channels. Various organizations, including the World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC), and other prominent individuals issued high-profile advice on preventing the further spread of COVID-19.

Objective : The purpose of this study is to leverage machine learning and Twitter data from the pandemic period to explore health beliefs regarding mask wearing and vaccines and the influence of high-profile cues to action.

Methods : A total of 646,885,238 COVID-19-related English tweets were filtered, creating a mask-wearing data set and a vaccine data set. Researchers manually categorized a training sample of 3500 tweets for each data set according to their relevance to Health Belief Model (HBM) constructs and used coded tweets to train machine learning models for classifying each tweet in the data sets.

Results : In total, 5 models were trained for both the mask-related and vaccine-related data sets using the XLNet transformer model, with each model achieving at least 81% classification accuracy. Health beliefs regarding perceived benefits and barriers were most pronounced for both mask wearing and immunization; however, the strength of those beliefs appeared to vary in response to high-profile cues to action.

Conclusions : During both the COVID-19 pandemic and the infodemic, health beliefs related to perceived benefits and barriers observed through Twitter using a big data machine learning approach varied over time and in response to high-profile cues to action from prominent organizations and individuals.

Ke Si Yang, Neeley-Tass E Shannon, Barnes Michael, Hanson Carl L, Giraud-Carrier Christophe, Snell Quinn

COVID-19, Health Belief Model, Twitter, content analysis, deep learning, health belief, infodemic, infodemiology, machine learning, mask, misinformation, vaccination, vaccine data set

General General

Improved Deep Convolutional Neural Networks using Chimp Optimization Algorithm for Covid19 Diagnosis from the X-Ray Images.

In Expert systems with applications

Applying Deep Learning (DL) in radiological images (i.e., chest X-Rays) is emerging because of the necessity of having accurate and fast COVID-19 detectors. Deep Convolutional Neural Networks (DCNN) have been typically used as robust COVID-19 positive case detectors in these approaches. Such DCCNs tend to utilize Gradient Descent-Based (GDB) algorithms as the last fully-connected layers' trainers. Although GDB training algorithms have simple structures and fast convergence rates for cases with large training samples, they suffer from the manual tuning of numerous parameters, getting stuck in local minima, large training samples set requirements, and inherently sequential procedures. It is exceedingly challenging to parallelize them with Graphics Processing Units (GPU). Consequently, the Chimp Optimization Algorithm (ChOA) is presented for training the DCNN's fully connected layers in light of the scarcity of a big COVID-19 training dataset and for the purpose of developing a fast COVID-19 detector with the capability of parallel implementation. Following that, two publicly accessible datasets termed COVID-Xray-5k and COVIDetectioNet are used to benchmark the proposed detector known as DCCN-Chimp. In order to make a fair comparison, two structures are proposed: i-6c-2s-12c-2s and i-8c-2s-16c-2s, all of which have had their hyperparameters fine-tuned. The outcomes are evaluated in comparison to standard DCNN, Hybrid DCNN plus Genetic Algorithm (DCNN-GA), and Matched Subspace classifier with Adaptive Dictionaries (MSAD). Due to the large variation in results, we employ a weighted average of the ensemble of ten trained DCNN-ChOA, with the validation accuracy of the weights being used to determine the final weights. The validation accuracy for the mixed ensemble DCNN-ChOA is 99.11%. LeNet-5 DCNN's ensemble detection accuracy on COVID-19 is 84.58%. Comparatively, the suggested DCNN-ChOA yields over 99.11% accurate detection with a false alarm rate of less than 0.89 %. The outcomes show that the DCCN-Chimp can deliver noticeably superior results than the comparable detectors. The Class Activation Map (CAM) is another tool used in this study to identify probable COVID-19-infected areas. Results show that highlighted regions are completely connected with clinical outcomes, which has been verified by experts.

Cai Chengfeng, Gou Bingchen, Khishe Mohammad, Mohammadi Mokhtar, Rashidi Shima, Moradpour Reza, Mirjalili Seyedali

2022-Nov-04

COVID-19 diagnosis, Chest X-Rays, Chimp Optimization Algorithm, Convolutional Neural Networks, Deep Learning

General General

Preparations for the Assessment of COVID-19 Infection and Long-Term Cardiovascular Risk.

In Korean circulation journal

Studies showing that coronavirus disease 2019 (COVID-19) is associated with an increased risk of cardiovascular disease continue to be published. However, studies on how long the overall cardiovascular risk increases after COVID-19 and the magnitude of its long-term effects have only been confirmed recently. This is partly because the distinction between cardiovascular risk as an acute complication of COVID-19 or post-acute cardiovascular manifestations is ambiguous. Long-COVID has arisen as an important topic in the second half of the pandemic. This term indicates that symptoms persist for more than two 2 months; following three months of SARS-CoV-2 infection and cannot be explained by other medical conditions. Despite the agreement of these international organizations and experts, it is difficult to define whether there is sufficient medical evidence to prove the existence of long-COVID. However, the Korean government and Korea Disease Control and Prevention Agency (KDCA) are preparing a new platform to assess the long-term impact of COVID-19. Using this data, a prospective cohort of 10,000 confirmed COVID-19 cases will be established. This cohort will be linked with claims data from the National Health Insurance Services (NHIS) and it is expected that increased real-world evidence of long-COVID will be accumulated.

Jung Jaehun

2022-Nov

COVID-19, COVID-19 vaccination, Cardiovascular risk, Long-COVID

General General

Intelligent COVID-19 screening platform based on breath analysis.

In Journal of breath research

BACKGROUND : The spread of COVID-19 results in an increasing incidence and mortality. The typical diagnosis technique for SARS-CoV-2 infection is RT-PCR, which is relatively expensive, time-consuming, professional, and suffered from false-negative results. A reliable, non-invasive diagnosis method is in urgent need for the rapid screening of COVID-19 patients and controlling the epidemic.

METHODS : Here we constructed an intelligent system based on the VOC biomarkers in human breath combined with machine learning models. The VOC profiles of 122 breath samples (65 of COVID-19 infections and 57 of controls) were identified with a portable gas chromatograph-mass spectrometer. Among them, eight VOCs exhibited significant differences (p<0.001) between the COVID-19 group and the control group. The cross-validation algorithm optimized support vector machine (SVM) model was employed for the prediction of COVID-19 infection.

RESULTS : The proposed SVM model performed a powerful capability in discriminating COVID-19 patients from healthy controls, with an accuracy of 97.3%, a sensitivity of 100%, a specificity of 94.1%, and a precision of 95.2%, and an F1 score of 97.6%. The SVM model was also compared with other common machine models, including artificial neural network, k-nearest neighbor, and logistic regression, and demonstrated obvious superiority in the prediction of COVID-19 infection. Furthermore, user-friendly software was developed based on the optimized SVM model.

CONCLUSION : The developed intelligent platform based on breath analysis provides a new strategy for the point-of-care screening of COVID and shows great potential in clinical application.

Xue Cuili, Xu Xiaohong, Liu Zexi, Zhang Yuna, Xu Yuli, Niu Jiaqi, Jin Han, Xiong Wujun, Cui Daxiang

2022-Nov-08

COVID-19 diagnosis, Portable GC-MS, breath analysis, support vector machine, volatile organic compound

Public Health Public Health

Exploring public opinion about telehealth during COVID-19 by social media analytics.

In Journal of telemedicine and telecare ; h5-index 28.0

While COVID-19 catalyzed the acceptance and use of telehealth, our understanding of how it is perceived by multi-stakeholders such as patients, clinicians, and health authorities is limited. Drawing on social media analytics, this research examines social media discourses and users' opinions about telehealth during the COVID-19 pandemic. It applies natural language processing and deep learning to explore word of mouth on telehealth with a contextualized focus on the COVID-19 pandemic. We conducted topic modeling, sentiment analysis, and emotion analysis (fearful, happy, sad, surprised, and angry emotions). The topic modeling analysis led to the identification of 18 topics, representing 6 themes of digital health service delivery, pandemic response, communication and promotion, government action, health service domains (e.g. mental health, cancer, aged care), as well as pharma and drug. The sentiment analysis revealed that while most opinions expressed in tweets were positive, the public expressed mostly negative opinions about certain aspects of COVID-19 such as lockdowns and cyberattacks. Emotion analysis of tweets showed a dominant pattern of fearful and sad emotions in particular topics. The results of this study that inductively emerged from our social media analysis can aid public health authorities and health professionals to address the concerns of telehealth users and improve their experiences.

Pool Javad, Namvar Morteza, Akhlaghpour Saeed, Fatehi Farhad

2022-Dec

COVID-19, Telehealth, machine learning, social media analytics, telemedicine

General General

Personalized Prediction of Response to Smartphone-Delivered Meditation Training: Randomized Controlled Trial.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Meditation apps have surged in popularity in recent years, with an increasing number of individuals turning to these apps to cope with stress, including during the COVID-19 pandemic. Meditation apps are the most commonly used mental health apps for depression and anxiety. However, little is known about who is well suited to these apps.

OBJECTIVE : This study aimed to develop and test a data-driven algorithm to predict which individuals are most likely to benefit from app-based meditation training.

METHODS : Using randomized controlled trial data comparing a 4-week meditation app (Healthy Minds Program [HMP]) with an assessment-only control condition in school system employees (n=662), we developed an algorithm to predict who is most likely to benefit from HMP. Baseline clinical and demographic characteristics were submitted to a machine learning model to develop a "Personalized Advantage Index" (PAI) reflecting an individual's expected reduction in distress (primary outcome) from HMP versus control.

RESULTS : A significant group × PAI interaction emerged (t658=3.30; P=.001), indicating that PAI scores moderated group differences in outcomes. A regression model that included repetitive negative thinking as the sole baseline predictor performed comparably well. Finally, we demonstrate the translation of a predictive model into personalized recommendations of expected benefit.

CONCLUSIONS : Overall, the results revealed the potential of a data-driven algorithm to inform which individuals are most likely to benefit from a meditation app. Such an algorithm could be used to objectively communicate expected benefits to individuals, allowing them to make more informed decisions about whether a meditation app is appropriate for them.

TRIAL REGISTRATION : ClinicalTrials.gov NCT04426318; https://clinicaltrials.gov/ct2/show/NCT04426318.

Webb Christian A, Hirshberg Matthew J, Davidson Richard J, Goldberg Simon B

2022-Nov-08

machine learning, meditation, mobile phone, mobile technology, precision medicine, prediction, smartphone app

General General

The positive energy of netizens: development and application of fine-grained sentiment lexicon and emotional intensity model.

In Current psychology (New Brunswick, N.J.)

The outbreak of COVID-19 has led to a global health crisis and caused huge emotional swings. However, the positive emotional expressions, like self-confidence, optimism, and praise, that appear in Chinese social networks are rarely explored by researchers. This study aims to analyze the characteristics of netizens' positive energy expressions and the impact of node events on public emotional expression during the COVID-19 pandemic. First, a total of 6,525,249 Chinese texts posted by Sina Weibo users were randomly selected through textual data cleaning and word segmentation for corpus construction. A fine-grained sentiment lexicon that contained POSITIVE ENERGY was built using Word2Vec technology; this lexicon was later used to conduct sentiment category analysis on original posts. Next, through manual labeling and multi-classification machine learning model construction, four mainstream machine learning algorithms were selected to train the emotional intensity model. Finally, the lexicon and optimized emotional intensity model were used to analyze the emotional expressions of Chinese netizens. The results show that POSITIVE ENERGY expression accounted for 40.97% during the COVID-19 pandemic. Over the course of time, POSITIVE ENERGY emotions were displayed at the highest levels and SURPRISES the lowest. The analysis results of the node events showed after the outbreak was confirmed officially, the expressions of POSITIVE ENERGY and FEAR increased simultaneously. After the initial victory in pandemic prevention and control, the expression of POSITIVE ENERGY and SAD reached a peak, while the increase of SAD was the most prominent. The fine-grained sentiment lexicon, which includes a POSITIVE ENERGY category, demonstrated reliable algorithm performance and can be used for sentiment classification of Chinese Internet context. We also found many POSITIVE ENERGY expressions in Chinese online social platforms which are proven to be significantly affected by nod events of different nature.

Pan Wenhao, Han Yingying, Li Jinjin, Zhang Emily, He Bikai

2022-Nov-03

COVID-19 pandemic, Fine-grained sentiment lexicon, Positive energy, Social media analysis

General General

IoT-Based COVID-19 Diagnosing and Monitoring Systems: A Survey.

In IEEE access : practical innovations, open solutions

To date, the novel Coronavirus (SARS-CoV-2) has infected millions and has caused the deaths of thousands of people around the world. At the moment, five antibodies, two from China, two from the U.S., and one from the UK, have already been widely utilized and numerous vaccines are under the trail process. In order to reach herd immunity, around 70% of the population would need to be inoculated. It may take several years to hinder the spread of SARS-CoV-2. Governments and concerned authorities have taken stringent measurements such as enforcing partial, complete, or smart lockdowns, building temporary medical facilities, advocating social distancing, and mandating masks in public as well as setting up awareness campaigns. Furthermore, there have been massive efforts in various research areas and a wide variety of tools, technologies and techniques have been explored and developed to combat the war against this pandemic. Interestingly, machine learning (ML) algorithms and internet of Things (IoTs) technology are the pioneers in this race. Up till now, several real-time and intelligent IoT-based COVID-19 diagnosing, and monitoring systems have been proposed to tackle the pandemic. In this article we have analyzed a wide range of IoTs technologies which can be used in diagnosing and monitoring the infected individuals and hotspot areas. Furthermore, we identify the challenges and also provide our vision about the future research on COVID-19.

Anjum Nasreen, Alibakhshikenari Mohammad, Rashid Junaid, Jabeen Fouzia, Asif Amna, Mohamed Ehab Mahmoud, Falcone Francisco

2022

COVID-19 pandemic, Internet of Things (IoTs), artificial intelligence (AI), coronavirus, machine learning algorithms

General General

The paradigm and future value of the metaverse for the intervention of cognitive decline.

In Frontiers in public health

Cognitive decline is a gradual neurodegenerative process that is affected by genetic and environmental factors. The doctor-patient relationship in the healthcare for cognitive decline is in a "shallow" medical world. With the development of data science, virtual reality, artificial intelligence, and digital twin, the introduction of the concept of the metaverse in medicine has brought alternative and complementary strategies in the intervention of cognitive decline. This article technically analyzes the application scenarios and paradigms of the metaverse in medicine in the field of mental health, such as hospital management, diagnosis, prediction, prevention, rehabilitation, progression delay, assisting life, companionship, and supervision. The metaverse in medicine has made primary progress in education, immersive consultation, dental disease, and Parkinson's disease, bringing revolutionary prospects for non-pharmacological complementary treatment of cognitive decline and other mental problems. In particular, with the demand for non-face-to-face communication generated by the global COVID-19 epidemic, the needs for uncontactable healthcare service for the elderly have increased. The paradigm of self-monitoring, self-healing, and healthcare experienced by the elderly through the metaverse in medicine, especially from meta-platform, meta-community, and meta-hospital, will be generated, which will reconstruct the service modes for the elderly people. The future map of the metaverse in medicine is huge, which depends on the co-construction of community partners.

Zhou Hao, Gao Jian-Yi, Chen Ying

2022

“Alzheimers disease”, cognitive decline, digital twin, mental health, metaverse in medicine, virtual reality

General General

Interpreting global variations in the toll of COVID-19: The case for context and nuance in hypothesis generation and testing.

In Frontiers in public health

Key points : As of January 2022, the COVID-19 pandemic was on-going, affecting populations worldwide. The potential risks of the Omicron variant (and future variants) still remain an area of active investigation. Thus, the ultimate human toll of SARS-CoV-2, and, by extension, the variations in that toll among diverse populations, remain unresolved. Nonetheless, an extensive literature on causal factors in the observed patterns of COVID-19 morbidity and cause-specific mortality has emerged-particularly at the aggregate level of analysis. This article explores potential pitfalls in the attribution of COVID outcomes to specific factors in isolation by examining a diverse set of potential factors and their interactions.

Methods : We sourced published data to establish a global database of COVID-19 outcomes for 68 countries and augmented these with an array of potential explanatory covariates from a diverse set of sources. We sought population-level aggregate factors from both health- and (traditionally) non-health domains, including: (a) Population biomarkers (b) Demographics and infrastructure (c) Socioeconomics (d) Policy responses at the country-level. We analyzed these data using (OLS) regression and more flexible non-parametric methods such as recursive partitioning, that are useful in examining both potential joint factor contributions to variations in pandemic outcomes, and the identification of possible interactions among covariates across these domains.

Results : Using the national obesity rates of 68 countries as an illustrative predictor covariate of COVID-19 outcomes, we observed marked inconsistencies in apparent outcomes by population. Importantly, we also documented important variations in outcomes, based on interactions of health factors with covariates in other domains that are traditionally not related to biomarkers. Finally, our results suggest that single-factor explanations of population-level COVID-19 outcomes (e.g., obesity vs. cause-specific mortality) appear to be confounded substantially by other factors.

Conclusions/implications : Our methods and findings suggest that a full understanding of the toll of the COVID-19 pandemic, as would be central to preparing for similar future events, requires analysis within and among diverse variable domains, and within and among diverse populations. While this may seem apparent, the bulk of the recent literature on the pandemic has focused on one or a few of these drivers in isolation. Hypothesis generation and testing related to pandemic outcomes will benefit from accommodating the nuance of covariate interactions, in an epidemiologic context. Finally, our results add to the literature on the ecological fallacy: the attempt to infer individual drivers and outcomes from the study of population-level aggregates.

Stein Roger M, Katz David L

2022

COVID, health economics, health policy, lifestyle factors, machine learning, obesity, pandemic, statistical methods

General General

Ensemble learning-based feature selection for phosphorylation site detection.

In Frontiers in genetics ; h5-index 62.0

SARS-COV-2 is prevalent all over the world, causing more than six million deaths and seriously affecting human health. At present, there is no specific drug against SARS-COV-2. Protein phosphorylation is an important way to understand the mechanism of SARS -COV-2 infection. It is often expensive and time-consuming to identify phosphorylation sites with specific modified residues through experiments. A method that uses machine learning to make predictions about them is proposed. As all the methods of extracting protein sequence features are knowledge-driven, these features may not be effective for detecting phosphorylation sites without a complete understanding of the mechanism of protein. Moreover, redundant features also have a great impact on the fitting degree of the model. To solve these problems, we propose a feature selection method based on ensemble learning, which firstly extracts protein sequence features based on knowledge, then quantifies the importance score of each feature based on data, and finally uses the subset of important features as the final features to predict phosphorylation sites.

Liu Songbo, Cui Chengmin, Chen Huipeng, Liu Tong

2022

SARS-cov-2, ensemble learning (EN), feature selection (FS), marchine-learning, phosphorylation site

General General

Attention based parameter estimation and states forecasting of COVID-19 pandemic using modified SIQRD Model.

In Chaos, solitons, and fractals

In this work, we propose a new mathematical modelling of the spread of COVID-19 infection in an arbitrary population, by modifying the SIQRD model as m-SIQRD model, while taking into consideration the eight governmental interventions such as cancellation of events, closure of public places etc., as well as the influence of the asymptomatic cases on the states of the model. We introduce robustness and improved accuracy in predictions of these models by utilizing a novel deep learning scheme. This scheme comprises of attention based architecture, alongside with Generative Adversarial Network (GAN) based data augmentation, for robust estimation of time varying parameters of m-SIQRD model. In this regard, we also utilized a novel feature extraction methodology by employing noise removal operation by Spline interpolation and Savitzky-Golay filter, followed by Principal Component Analysis (PCA). These parameters are later directed towards two main tasks: forecasting of states to the next 15 days, and estimation of best policy encodings to control the infected and deceased number within the framework of data driven synergetic control theory. We validated the superiority of the forecasting performance of the proposed scheme over countries of South Korea and Germany and compared this performance with 7 benchmark forecasting models. We also showed the potential of this scheme to determine best policy encodings in South Korea for 15 day forecast horizon.

Khan Junaid Iqbal, Ullah Farman, Lee Sungchang

2022-Oct-31

COVID-19, Compartment model, Control theory, Deep learning

Public Health Public Health

Leveraging OpenStreetMap and Multimodal Remote Sensing Data with Joint Deep Learning for Wastewater Treatment Plants Detection.

In International journal of applied earth observation and geoinformation : ITC journal

Humans rely on clean water for their health, well-being, and various socio-economic activities. During the past few years, the COVID-19 pandemic has been a constant reminder of about the importance of hygiene and sanitation for public health. The most common approach to securing clean water supplies for this purpose is via wastewater treatment. To date, an effective method of detecting wastewater treatment plants (WWTP) accurately and automatically via remote sensing is unavailable. In this paper, we provide a solution to this task by proposing a novel joint deep learning (JDL) method that consists of a fine-tuned object detection network and a multi-task residual attention network (RAN). By leveraging OpenStreetMap (OSM) and multimodal remote sensing (RS) data, our JDL method is able to simultaneously tackle two different tasks: land use land cover (LULC) and WWTP classification. Moreover, JDL exploits the complementary effects between these tasks for a performance gain. We train JDL using 4,187 WWTP features and 4,200 LULC samples and validate the performance of the proposed method over a selected area around Stuttgart with 723 WWTP features and 1,200 LULC samples to generate an LULC classification map and a WWTP detection map. Extensive experiments conducted with different comparative methods demonstrate the effectiveness and efficiency of our JDL method in automatic WWTP detection in comparison with single-modality/single-task or traditional survey methods. Moreover, lessons learned pave the way for future works to simultaneously and effectively address multiple large-scale mapping tasks (e.g., both mapping LULC and detecting WWTP) from multimodal RS data via deep learning.

Li Hao, Zech Johannes, Hong Danfeng, Ghamisi Pedram, Schultz Michael, Zipf Alexander

2022-Jun

GeoAI, OpenStreetMap, SDG 6, multi-task learning, multimodal, object detection, volunteered geographic information, wastewater treatment

General General

Socially facilitative robots for older adults to alleviate social isolation: A participatory design workshop approach in the US and Japan.

In Frontiers in psychology ; h5-index 92.0

Social technology can improve the quality of older adults' social lives and mitigate negative mental and physical health outcomes associated with loneliness, but it should be designed collaboratively with this population. In this paper, we used participatory design (PD) methods to investigate how robots might be used as social facilitators for middle-aged and older adults (age 50+) in both the US and Japan. We conducted PD workshops in the US and Japan because both countries are concerned about the social isolation of these older adults due to their rapidly aging populations. We developed a novel approach to participatory design of future technologies that spends 2/3 of the PD session asking participants about their own life experiences as a foundation. This grounds the conversation in reality, creates rapport among the participants, and engages them in creative critical thinking. Then, we build upon this foundation, pose an abstract topic, and ask participants to brainstorm on the topic based on their previous discussion. In both countries, participants were eager to actively discuss design ideas for socially facilitative robots and imagine how they might improve their social lives. US participants suggested design ideas for telepresence robots, social distancing robots, and social skills artificial intelligence programs, while Japanese participants suggested ideas for pet robots, robots for sharing experiences, and easy-to-operate instructor robots. Comparing these two countries, we found that US participants saw robots as tools to help facilitate their social connections, while Japanese participants envisioned robots to function as surrogate companions for their parents and distract them from loneliness when they were unavailable. With this paper, we contribute to the literature in two main ways, presenting: (1) A novel approach to participatory design of future technologies that grounds participants in their everyday experience, and (2) Results of the study indicating how middle-aged and older adults from the US and Japan wanted technologies to improve their social lives. Although we conducted the workshops during the COVID-19 pandemic, many findings generalized to other situations related to social isolation, such as older adults living alone.

Fraune Marlena R, Komatsu Takanori, Preusse Harrison R, Langlois Danielle K, Au Rachel H Y, Ling Katrina, Suda Shogo, Nakamura Kiko, Tsui Katherine M

2022

Japan, US, cross-cultural study, experience-grounded participatory design, human-robot interaction, older adults, social isolation, social robots

Surgery Surgery

"The show must go on": Aftermath of Covid-19 on anesthesiology residency programs.

In Saudi journal of anaesthesia

COVID-19 has caused tectonic changes in the personal and professional lives of anesthesiologists and, among several aspects, anesthesiology residency and sub-specialty training has also undergone an unforeseen overhaul. We read the articles published on the impact of COVID-19 on training of anesthesiologists and set out to extract and narrate all the significant observations. At the outset, we begin by explaining how this pandemic posed a threat to the safety of the residents and mitigating measures like PPE and barriers that have now become 'the new normal'. Sub-specialties like critical care, cardiac anesthesia, pain and palliative care have also faced difficulty in imparting training due to an initial dearth in elective surgery case load but have adapted innovative measures to overcome that. Initially, conducting thesis and research became difficult due to problems in achieving the desires sample size needed to get significant results, but this pandemic has emerged as a dynamic laboratory where topics like 'psychological impact of COVID-19' and 'development of artificial intelligence models in COVID -19 ICUs' came into the fore. Pattern of examination has also become virtual and webinars showed how knowledge, with the right medium, has the potential of global outreach. As the pandemic took a toll on the mental health of the residents, attention was paid to this previously neglected aspect and ensuring their emotional well-being became a priority to avoid the issue of burn-out. We comment on how what initially was considered a scary problem, actually paved way for growth. It brought attention to safety, innovation, new tools for training, finding solutions within constraints, continuing developing our residents into future leaders who were also trained for mitigating disasters. Changes like online education, research on socio-economic impact, priority to mental health and artificial intelligence are here to stay and by imbibing it, we ensure that 'the show must go on'.

Jaju Rishabh, Saxena Medhavi, Paliwal Naveen, Bihani Pooja, Tharu Vidya

Academic, Covid-19, anesthesia, burnout, resident training

Public Health Public Health

COVID-19 Outbreak Forecasting Based on Vaccine Rates and Tweets Classification.

In Computational intelligence and neuroscience

The spread of COVID-19 has affected more than 200 countries and has caused serious public health concerns. The infected cases are on the increase despite the effectiveness of the vaccines. An efficient and quick surveillance system for COVID-19 can help healthcare decision-makers to contain the virus spread. In this study, we developed a novel framework using machine learning (ML) models capable of detecting COVID-19 accurately at an early stage. To estimate the risks, many models use social networking sites (SNSs) in tracking the disease outbreak. Twitter is one of the SNSs that is widely used to create an efficient resource for disease real-time analysis and can provide an early warning for health officials. We introduced a pipeline framework of outbreak prediction that incorporates a first-step hybrid method of word embedding for tweet classification. In the second step, we considered the classified tweets with external features such as vaccine rate associated with infected cases passed to machine learning algorithms for daily predictions. Thus, we applied different machine learning models such as the SVM, RF, and LR for classification and the LSTM, Prophet, and SVR for prediction. For the hybrid word embedding techniques, we applied TF-IDF, FastText, and Glove and a combination of the three features to enhance the classification. Furthermore, to improve the forecast performance, we incorporated vaccine data as input together with tweets and confirmed cases. The models' performance is more than 80% accurate, which shows the reliability of the proposed study.

Didi Y, Walha A, Ben Halima M, Wali A

2022

General General

Dual-Proxy Modeling for Masked Face Recognition.

In Procedia computer science

With the recent worldwide COVID-19 pandemic, almost everyone wears a mask daily, leading to severe degradation in the accuracy of conventional face recognition systems. Several works improve the performance of masked faces by adopting synthetic masked face images for training. However, such methods often cause performance degradation on unmasked faces, raising the contradiction between the face recognition system's accuracy on unmasked and masked faces. In this paper, we propose a dual-proxy face recognition training method to improve masked faces' performance while maintaining unmasked faces' performance. Specifically, we design two fully-connected layers as the unmasked and masked feature space proxies to alleviate the significant difference between the two data distributions. The cross-space constraints are adopted to ensure the intra-class compactness and inter-class discrepancy. Extensive experiments on popular unmasked face benchmarks and masked face benchmarks, including real-world mask faces and the generated mask faces, demonstrate our method's superiority over the state-of-the-art methods on masked faces without incurring a notable accuracy degradation on unmasked faces.

Shuhui Wang, Xiaochen Mao

2022

Dual-Proxy, Masked face recognition, deep learning, neural network

General General

Care for the Mind Amid Chronic Diseases: An Interpretable AI Approach Using IoT

ArXiv Preprint

Health sensing for chronic disease management creates immense benefits for social welfare. Existing health sensing studies primarily focus on the prediction of physical chronic diseases. Depression, a widespread complication of chronic diseases, is however understudied. We draw on the medical literature to support depression prediction using motion sensor data. To connect human expertise in the decision-making, safeguard trust for this high-stake prediction, and ensure algorithm transparency, we develop an interpretable deep learning model: Temporal Prototype Network (TempPNet). TempPNet is built upon the emergent prototype learning models. To accommodate the temporal characteristic of sensor data and the progressive property of depression, TempPNet differs from existing prototype learning models in its capability of capturing the temporal progression of depression. Extensive empirical analyses using real-world motion sensor data show that TempPNet outperforms state-of-the-art benchmarks in depression prediction. Moreover, TempPNet interprets its predictions by visualizing the temporal progression of depression and its corresponding symptoms detected from sensor data. We further conduct a user study to demonstrate its superiority over the benchmarks in interpretability. This study offers an algorithmic solution for impactful social good - collaborative care of chronic diseases and depression in health sensing. Methodologically, it contributes to extant literature with a novel interpretable deep learning model for depression prediction from sensor data. Patients, doctors, and caregivers can deploy our model on mobile devices to monitor patients' depression risks in real-time. Our model's interpretability also allows human experts to participate in the decision-making by reviewing the interpretation of prediction outcomes and making informed interventions.

Jiaheng Xie, Xiaohang Zhao, Xiang Liu, Xiao Fang

2022-11-08

General General

Insight into the nonlinear effect of COVID-19 on well-being in China: Commuting, a vital ingredient.

In Journal of transport & health

Background : COVID-19 had a devastating impact on people's work, travel, and well-being worldwide. As one of the first countries to be affected by the virus and develop relatively well-executed pandemic control, China has witnessed a significant shift in people's well-being and habits, related to both commuting and social interaction. In this context, what factors and the extent to which they contribute to well-being are worth exploring.

Methods : Through a questionnaire survey within mainland China, 688 valid sheets were collected, capturing various aspects of individuals' life, including travel, and social status. Focusing on commuting and other factors, a Gradient Boosting Decision Tree (GBDT) model was developed based on 300 sheets reporting working trips, to analyze the effects on well-being. Two indicators, i.e., the Relative Importance (RI) and Partial Dependency Plot (PDP), were used to quantify and visualize the effects of the explanatory factors and the synergy among them.

Results : Commuting characteristics are the most critical ingredients, followed by social interactions to explain subjective well-being. Commuting stress poses the most substantial effect. Less stressful commuting trips can solidly improve overall well-being. Better life satisfaction is linked with shorter confinement periods and increased restriction levels. Meanwhile, the switch from in-person to online social interactions had less impact on young people's life satisfaction. Older people were unsatisfied with this change, which had a significant negative impact on their life satisfaction.

Conclusions : From the synergy of commuting stress and commuting time on well-being, the effect of commuting time on well-being is mediated by commuting stress in the case of China. Even if one is satisfied with online communication, the extent of enhancement on well-being is minimal, for it still cannot replace face-to-face interaction. The findings can be beneficial in improving the overall well-being of society during the pandemic and after the virus has been eradicated.

Dong Yinan, Sun Yilin, D Waygood E Owen, Wang Bobin, Huang Pei, Naseri Hamed

2022-Oct-31

COVID-19, Commuting behavior, Machine learning, Social interaction, Well-being

General General

A Critical Review of Global Digital Divide and the Role of Technology in Healthcare.

In Cureus

Healthcare and technology, the fusion of these two distinct sciences can be traced back to the Vedic era. Regrettably, while it is evident that the journey of advancements in knowledge and innovation leading to the advent of technology to better the health of mankind is not a recent one, owing to inexistent means of transfer of knowledge, these contraptions stayed mostly localized to the regions of their inventors. This article seeks to review the vital role that technology has in bettering the health status of the global community and the challenges associated with healthcare technologies like inequity in connectivity, affordability, and accessibility. Technology and artificial intelligence are integrated to the best of the health systems across the world but these advancements are not accessible to a considerable part of the global population. While affordability, the absence of a steady internet supply, and the lack of a device to use the technology are the major impediments causing this digital divide, cultural factors and health literacy also contribute to this scenario. Nevertheless, access to the internet has been recognized as a basic need by all governments around the globe. The COVID-19 pandemic shook the health systems of developed and developing countries alike and has made every administration feel the urgency in making healthcare more accessible. Having seamless internet coverage and setups to make telemedicine or online consultations possible, can contribute significantly in paving the path to making our societies prosperous and healthier. With the world's consensus about this goal, efforts now should be focused on research and development for making these technologies more affordable and accessible without compromising their utility.

Reddy Himabindu, Joshi Shiv, Joshi Abhishek, Wagh Vasant

2022-Sep

digital divide, digital health, e-health, healthcare access, healthcare technology, inequity

Surgery Surgery

Homogeneous ensemble models for predicting infection levels and mortality of COVID-19 patients: Evidence from China.

In Digital health

Background : Persistence of long-term COVID-19 pandemic is putting high pressure on healthcare services worldwide for several years. This article aims to establish models to predict infection levels and mortality of COVID-19 patients in China.

Methods : Machine learning models and deep learning models have been built based on the clinical features of COVID-19 patients. The best models are selected by area under the receiver operating characteristic curve (AUC) scores to construct two homogeneous ensemble models for predicting infection levels and mortality, respectively. The first-hand clinical data of 760 patients are collected from Zhongnan Hospital of Wuhan University between 3 January and 8 March 2020. We preprocess data with cleaning, imputation, and normalization.

Results : Our models obtain AUC = 0.7059 and Recall (Weighted avg) = 0.7248 in predicting infection level, while AUC=0.8436 and Recall (Weighted avg) = 0.8486 in predicting mortality ratio. This study also identifies two sets of essential clinical features. One is C-reactive protein (CRP) or high sensitivity C-reactive protein (hs-CRP) and the other is chest tightness, age, and pleural effusion.

Conclusions : Two homogeneous ensemble models are proposed to predict infection levels and mortality of COVID-19 patients in China. New findings of clinical features for benefiting the machine learning models are reported. The evaluation of an actual dataset collected from January 3 to March 8, 2020 demonstrates the effectiveness of the models by comparing them with state-of-the-art models in prediction.

Wang Jiafeng, Zhou Xianlong, Hou Zhitian, Xu Xiaoya, Zhao Yueyue, Chen Shanshan, Zhang Jun, Shao Lina, Yan Rong, Wang Mingshan, Ge Minghua, Hao Tianyong, Tu Yuexing, Huang Haijun

COVID-19, Ensemble model, electronic health records, machine learning, prediction models

Radiology Radiology

Mental health and chest CT scores mediate the relationship between COVID-19 vaccination status and seroconversion time: A cross-sectional observational study in B.1.617.2 (Delta) infection patients.

In Frontiers in public health

Background : The coronavirus disease (COVID-19) pandemic, which has been ongoing for more than 2 years, has become one of the largest public health issues. Vaccination against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is one of the most important interventions to mitigate the COVID-19 pandemic. Our objective is to investigate the relationship between vaccination status and time to seroconversion.

Methods : We conducted a cross-sectional observational study during the SARS-CoV-2 B.1.617.2 outbreak in Jiangsu, China. Participants who infected with the B.1.617.2 variant were enrolled. Cognitive performance, quality of life, emotional state, chest computed tomography (CT) score and seroconversion time were evaluated for each participant. Statistical analyses were performed using one-way ANOVA, univariate and multivariate regression analyses, Pearson correlation, and mediation analysis.

Results : A total of 91 patients were included in the analysis, of whom 37.3, 25.3, and 37.3% were unvaccinated, partially vaccinated, and fully vaccinated, respectively. Quality of life was impaired in 30.7% of patients, especially for mental component summary (MCS) score. Vaccination status, subjective cognitive decline, and depression were risk factors for quality-of-life impairment. The chest CT score mediated the relationship of vaccination status with the MCS score, and the MCS score mediated the relationship of the chest CT score with time to seroconversion.

Conclusion : Full immunization course with an inactivated vaccine effectively lowered the chest CT score and improved quality of life in hospitalized patients. Vaccination status could influence time to seroconversion by affecting CT score and MCS score indirectly. Our study emphasizes the importance of continuous efforts in encouraging a full vaccination course.

Zhang Wen, Chen Qian, Dai Jinghong, Lu Jiaming, Li Jie, Yi Yongxiang, Fu Linqing, Li Xin, Liu Jiani, Liufu Jinlong, Long Cong, Zhang Bing

2022

B.1.617.2 Delta variant, COVID-19, SARS-CoV-2, mental health, seroconversion time, vaccination

General General

CELLS: A Parallel Corpus for Biomedical Lay Language Generation

ArXiv Preprint

Recent lay language generation systems have used Transformer models trained on a parallel corpus to increase health information accessibility. However, the applicability of these models is constrained by the limited size and topical breadth of available corpora. We introduce CELLS, the largest (63k pairs) and broadest-ranging (12 journals) parallel corpus for lay language generation. The abstract and the corresponding lay language summary are written by domain experts, assuring the quality of our dataset. Furthermore, qualitative evaluation of expert-authored plain language summaries has revealed background explanation as a key strategy to increase accessibility. Such explanation is challenging for neural models to generate because it goes beyond simplification by adding content absent from the source. We derive two specialized paired corpora from CELLS to address key challenges in lay language generation: generating background explanations and simplifying the original abstract. We adopt retrieval-augmented models as an intuitive fit for the task of background explanation generation, and show improvements in summary quality and simplicity while maintaining factual correctness. Taken together, this work presents the first comprehensive study of background explanation for lay language generation, paving the path for disseminating scientific knowledge to a broader audience. CELLS is publicly available at: https://github.com/LinguisticAnomalies/pls_retrieval.

Yue Guo, Wei Qiu, Gondy Leroy, Sheng Wang, Trevor Cohen

2022-11-07

General General

Prediction of COVID-19 patients in danger of death using radiomic features of portable chest radiographs.

In Journal of medical radiation sciences

INTRODUCTION : Computer-aided diagnostic systems have been developed for the detection and differential diagnosis of coronavirus disease 2019 (COVID-19) pneumonia using imaging studies to characterise a patient's current condition. In this radiomic study, we propose a system for predicting COVID-19 patients in danger of death using portable chest X-ray images.

METHODS : In this retrospective study, we selected 100 patients, including ten that died and 90 that recovered from the COVID-19-AR database of the Cancer Imaging Archive. Since it can be difficult to analyse portable chest X-ray images of patients with COVID-19 because bone components overlap with the abnormal patterns of this disease, we employed a bone-suppression technique during pre-processing. A total of 620 radiomic features were measured in the left and right lung regions, and four radiomic features were selected using the least absolute shrinkage and selection operator technique. We distinguished death from recovery cases using a linear discriminant analysis (LDA) and a support vector machine (SVM). The leave-one-out method was used to train and test the classifiers, and the area under the receiver-operating characteristic curve (AUC) was used to evaluate discriminative performance.

RESULTS : The AUCs for LDA and SVM were 0.756 and 0.959, respectively. The discriminative performance was improved when the bone-suppression technique was employed. When the SVM was used, the sensitivity for predicting disease severity was 90.9% (9/10), and the specificity was 95.6% (86/90).

CONCLUSIONS : We believe that the radiomic features of portable chest X-ray images can predict COVID-19 patients in danger of death.

Nakashima Maoko, Uchiyama Yoshikazu, Minami Hirotake, Kasai Satoshi

2022-Nov-05

Artificial intelligence, COVID-19, portable chest X-ray, prognosis prediction, radiomics

oncology Oncology

Machine Learning Successfully Detects Patients with COVID-19 Prior to PCR Results and Predicts Their Survival Based on Standard Laboratory Parameters in an Observational Study.

In Infectious diseases and therapy

INTRODUCTION : In the current COVID-19 pandemic, clinicians require a manageable set of decisive parameters that can be used to (i) rapidly identify SARS-CoV-2 positive patients, (ii) identify patients with a high risk of a fatal outcome on hospital admission, and (iii) recognize longitudinal warning signs of a possible fatal outcome.

METHODS : This comparative study was performed in 515 patients in the Maria Skłodowska-Curie Specialty Voivodeship Hospital in Zgierz, Poland. The study groups comprised 314 patients with COVID-like symptoms who tested negative and 201 patients who tested positive for SARS-CoV-2 infection; of the latter, 72 patients with COVID-19 died and 129 were released from hospital. Data on which we trained several machine learning (ML) models included clinical findings on admission and during hospitalization, symptoms, epidemiological risk, and reported comorbidities and medications.

RESULTS : We identified a set of eight on-admission parameters: white blood cells, antibody-synthesizing lymphocytes, ratios of basophils/lymphocytes, platelets/neutrophils, and monocytes/lymphocytes, procalcitonin, creatinine, and C-reactive protein. The medical decision tree built using these parameters differentiated between SARS-CoV-2 positive and negative patients with up to 90-100% accuracy. Patients with COVID-19 who on hospital admission were older, had higher procalcitonin, C-reactive protein, and troponin I levels together with lower hemoglobin and platelets/neutrophils ratio were found to be at highest risk of death from COVID-19. Furthermore, we identified longitudinal patterns in C-reactive protein, white blood cells, and D dimer that predicted the disease outcome.

CONCLUSIONS : Our study provides sets of easily obtainable parameters that allow one to assess the status of a patient with SARS-CoV-2 infection, and the risk of a fatal disease outcome on hospital admission and during the course of the disease.

Styrzynski Filip, Zhakparov Damir, Schmid Marco, Roqueiro Damian, Lukasik Zuzanna, Solek Julia, Nowicki Jakub, Dobrogowski Milosz, Makowska Joanna, Sokolowska Milena, Baerenfaller Katja

2022-Nov-04

COVID-19 prognosis, Laboratory parameters, Machine learning, Predictive features, SARS-CoV-2 diagnosis

General General

Detecting COVID-19 from digitized ECG printouts using 1D convolutional neural networks.

In PloS one ; h5-index 176.0

The COVID-19 pandemic has exposed the vulnerability of healthcare services worldwide, raising the need to develop novel tools to provide rapid and cost-effective screening and diagnosis. Clinical reports indicated that COVID-19 infection may cause cardiac injury, and electrocardiograms (ECG) may serve as a diagnostic biomarker for COVID-19. This study aims to utilize ECG signals to detect COVID-19 automatically. We propose a novel method to extract ECG signals from ECG paper records, which are then fed into one-dimensional convolution neural network (1D-CNN) to learn and diagnose the disease. To evaluate the quality of digitized signals, R peaks in the paper-based ECG images are labeled. Afterward, RR intervals calculated from each image are compared to RR intervals of the corresponding digitized signal. Experiments on the COVID-19 ECG images dataset demonstrate that the proposed digitization method is able to capture correctly the original signals, with a mean absolute error of 28.11 ms. The 1D-CNN model (SEResNet18), which is trained on the digitized ECG signals, allows to identify between individuals with COVID-19 and other subjects accurately, with classification accuracies of 98.42% and 98.50% for classifying COVID-19 vs. Normal and COVID-19 vs. other classes, respectively. Furthermore, the proposed method also achieves a high-level of performance for the multi-classification task. Our findings indicate that a deep learning system trained on digitized ECG signals can serve as a potential tool for diagnosing COVID-19.

Nguyen Thao, Pham Hieu H, Le Khiem H, Nguyen Anh-Tu, Thanh Tien, Do Cuong

2022

General General

Emerging Technologies Used in Health Management and Efficiency Improvement During Different Contact Tracing Phases Against COVID-19 Pandemic.

In IEEE reviews in biomedical engineering

Confronted with the COVID-19 health crisis, the year 2020 represented a turning point for the entire world. It paved the way for health-care systems to reaffirm their foundations by using different technologies such as sensors, wearables, mobile applications, drones, robots, Artificial Intelligence (AI), Machine Learning (ML) and the Internet of Things (IoT). A lot of domains have been renovated such as diagnosis, treatment, and monitoring, as well as previously unprecedented domains such as contact tracing. Contact tracing, in conjunction with the emergence, spread, and public compliance for vaccines, was a critical step for controlling and limiting the spread of the pandemic. Traditional contact tracing is usually dependent on individuals ability to recall their interactions, which is challenging and yet not effective. Consequently, further development and usage of automated, privacy-preserving, digital contact-tracing was required. As the pandemic is coming to an end, it is vital to collect and learn the effective used technologies that aided in fighting the virus in order to be prepared for any future pandemics and to be aware of any literature gaps that must be filled. This paper surveys state-of-the-art architectures, platforms, and applications combating COVID-19 at each phase of the five basic contact tracing phases, including case identification, contacts identification and rapid exposure notification, surveillance, regular follow up and prevention. In addition, there is a phase of preparation and post-pandemic services for current and needed future technology that will aid in the fight against any incoming infectious diseases.

Gendy Maggie Ezzat Gaber, Yuce Mehmet Rasit

2022-Nov-04

Radiology Radiology

Assessment of COVID-19 lung involvement on computed tomography by deep-learning-, threshold-, and human reader-based approaches-an international, multi-center comparative study.

In Quantitative imaging in medicine and surgery

Background : The extent of lung involvement in coronavirus disease 2019 (COVID-19) pneumonia, quantified on computed tomography (CT), is an established biomarker for prognosis and guides clinical decision-making. The clinical standard is semi-quantitative scoring of lung involvement by an experienced reader. We aim to compare the performance of automated deep-learning- and threshold-based methods to the manual semi-quantitative lung scoring. Further, we aim to investigate an optimal threshold for quantification of involved lung in COVID pneumonia chest CT, using a multi-center dataset.

Methods : In total 250 patients were included, 50 consecutive patients with RT-PCR confirmed COVID-19 from our local institutional database, and another 200 patients from four international datasets (n=50 each). Lung involvement was scored semi-quantitatively by three experienced radiologists according to the established chest CT score (CCS) ranging from 0-25. Inter-rater reliability was reported by the intraclass correlation coefficient (ICC). Deep-learning-based segmentation of ground-glass and consolidation was obtained by CT Pulmo Auto Results prototype plugin on IntelliSpace Discovery (Philips Healthcare, The Netherlands). Threshold-based segmentation of involved lung was implemented using an open-source tool for whole-lung segmentation under the presence of severe pathologies (R231CovidWeb, Hofmanninger et al., 2020) and consecutive quantitative assessment of lung attenuation. The best threshold was investigated by training and testing a linear regression of deep-learning and threshold-based results in a five-fold cross validation strategy.

Results : Median CCS among 250 evaluated patients was 10 [6-15]. Inter-rater reliability of the CCS was excellent [ICC 0.97 (0.97-0.98)]. Best attenuation threshold for identification of involved lung was -522 HU. While the relationship of deep-learning- and threshold-based quantification was linear and strong (r2 deep-learning vs. threshold=0.84), both automated quantification methods translated to the semi-quantitative CCS in a non-linear fashion, with an increasing slope towards higher CCS (r2 deep-learning vs. CCS= 0.80, r2 threshold vs. CCS=0.63).

Conclusions : The manual semi-quantitative CCS underestimates the extent of COVID pneumonia in higher score ranges, which limits its clinical usefulness in cases of severe disease. Clinical implementation of fully automated methods, such as deep-learning or threshold-based approaches (best threshold in our multi-center dataset: -522 HU), might save time of trained personnel, abolish inter-reader variability, and allow for truly quantitative, linear assessment of COVID lung involvement.

Fervers Philipp, Fervers Florian, Jaiswal Astha, Rinneburger Miriam, Weisthoff Mathilda, Pollmann-Schweckhorst Philip, Kottlors Jonathan, Carolus Heike, Lennartz Simon, Maintz David, Shahzad Rahil, Persigehl Thorsten

2022-Nov

Coronavirus disease 2019 (COVID-19), X-ray computed, biomarkers, linear models, pneumonia, tomography

General General

Pre-hospital prediction of adverse outcomes in patients with suspected COVID-19: Development, application and comparison of machine learning and deep learning methods.

In Computers in biology and medicine

BACKGROUND : COVID-19 infected millions of people and increased mortality worldwide. Patients with suspected COVID-19 utilised emergency medical services (EMS) and attended emergency departments, resulting in increased pressures and waiting times. Rapid and accurate decision-making is required to identify patients at high-risk of clinical deterioration following COVID-19 infection, whilst also avoiding unnecessary hospital admissions. Our study aimed to develop artificial intelligence models to predict adverse outcomes in suspected COVID-19 patients attended by EMS clinicians.

METHOD : Linked ambulance service data were obtained for 7,549 adult patients with suspected COVID-19 infection attended by EMS clinicians in the Yorkshire and Humber region (England) from 18-03-2020 to 29-06-2020. We used support vector machines (SVM), extreme gradient boosting, artificial neural network (ANN) models, ensemble learning methods and logistic regression to predict the primary outcome (death or need for organ support within 30 days). Models were compared with two baselines: the decision made by EMS clinicians to convey patients to hospital, and the PRIEST clinical severity score.

RESULTS : Of the 7,549 patients attended by EMS clinicians, 1,330 (17.6%) experienced the primary outcome. Machine Learning methods showed slight improvements in sensitivity over baseline results. Further improvements were obtained using stacking ensemble methods, the best geometric mean (GM) results were obtained using SVM and ANN as base learners when maximising sensitivity and specificity.

CONCLUSIONS : These methods could potentially reduce the numbers of patients conveyed to hospital without a concomitant increase in adverse outcomes. Further work is required to test the models externally and develop an automated system for use in clinical settings.

Hasan M, Bath P A, Marincowitz C, Sutton L, Pilbery R, Hopfgartner F, Mazumdar S, Campbell R, Stone T, Thomas B, Bell F, Turner J, Biggs K, Petrie J, Goodacre S

2022-Aug-28

Artificial neural networks, COVID-19, Emergency services, Extreme gradient boosting, Logistic regression, Stacking ensemble, Support vector machine

General General

D3AI-Spike: A deep learning platform for predicting binding affinity between SARS-CoV-2 spike receptor binding domain with multiple amino acid mutations and human angiotensin-converting enzyme 2.

In Computers in biology and medicine

The number of SARS-CoV-2 spike Receptor Binding Domain (RBD) with multiple amino acid mutations is huge due to random mutations and combinatorial explosions, making it almost impossible to experimentally determine their binding affinities to human angiotensin-converting enzyme 2 (hACE2). Although computational prediction is an alternative way, there is still no online platform to predict the mutation effect of RBD on the hACE2 binding affinity until now. In this study, we developed a free online platform based on deep learning models, namely D3AI-Spike, for quickly predicting binding affinity between spike RBD mutants and hACE2. The models based on CNN and CNN-RNN methods have the concordance index of around 0.8. Overall, the test results of the models are in agreement with the experimental data. To further evaluate the prediction power of D3AI-Spike, we predicted and experimentally determined the binding affinity of a VUM (variants under monitoring) variant IHU (B.1.640.2), which has fourteen amino acid substitutions, including N501Y and E484K, and 9 deletions located in the spike protein. The predicted average affinity score for wild-type RBD and IHU to hACE2 are 0.483 and 0.438, while the determined Kaff values are 5.39 ± 0.38 × 107 L/mol and 1.02 ± 0.47 × 107 L/mol, respectively, demonstrating the strong predictive power of D3AI-Spike. We think D3AI-Spike will be helpful to the viral transmission prediction for the new emerging SARS-CoV-2 variants. D3AI-Spike is now available free of charge at https://www.d3pharma.com/D3Targets-2019-nCoV/D3AI-Spike/index.php.

Han Jiaxin, Liu Tingting, Zhang Xinben, Yang Yanqing, Shi Yulong, Li Jintian, Ma Minfei, Zhu Weiliang, Gong Likun, Xu Zhijian

2022-Oct-25

COVID-19, D3AI-Spike, Deep learning, ELISA, Protein-protein interaction

General General

Superhuman performance on sepsis MIMIC-III data by distributional reinforcement learning.

In PloS one ; h5-index 176.0

We present a novel setup for treating sepsis using distributional reinforcement learning (RL). Sepsis is a life-threatening medical emergency. Its treatment is considered to be a challenging high-stakes decision-making problem, which has to procedurally account for risk. Treating sepsis by machine learning algorithms is difficult due to a couple of reasons: There is limited and error-afflicted initial data in a highly complex biological system combined with the need to make robust, transparent and safe decisions. We demonstrate a suitable method that combines data imputation by a kNN model using a custom distance with state representation by discretization using clustering, and that enables superhuman decision-making using speedy Q-learning in the framework of distributional RL. Compared to clinicians, the recovery rate is increased by more than 3% on the test data set. Our results illustrate how risk-aware RL agents can play a decisive role in critical situations such as the treatment of sepsis patients, a situation acerbated due to the COVID-19 pandemic (Martineau 2020). In addition, we emphasize the tractability of the methodology and the learning behavior while addressing some criticisms of the previous work (Komorowski et al. 2018) on this topic.

Böck Markus, Malle Julien, Pasterk Daniel, Kukina Hrvoje, Hasani Ramin, Heitzinger Clemens

2022

General General

Critical role of information and communication technology in nursing during the COVID-19 pandemic: A qualitative study.

In Journal of nursing management ; h5-index 43.0

AIM : To examine the need for information and communication technology (ICT)-based nursing care in improving patient management during the pandemic.

BACKGROUND : Maintaining traditional approaches to nursing in the ongoing coronavirus disease (COVID-19) pandemic predisposes healthcare systems to a risk of diminished quality of care. Using ICT (real-time videoconferencing, mobile robots, and artificial intelligence) could reduce burnout and infection risks by minimizing face-to-face contact.

METHOD : Qualitative descriptive design with content analysis.

RESULTS : Overall, 24 participants (14 nurses, six medical/nursing informatics experts, and four technology experts) were interviewed. Three main themes were extracted: Emerging challenges for nurses due to COVID-19, impact of new technology on patient and nurse experiences, and concerns with implementation of technology.

CONCLUSION : A significant portion of nurses' work was unrelated to professional nursing, causing burnout. ICT could help reduce nurses' burden by facilitating environmental management, non-contact communication, and providing emotional support for patients.

IMPLICATIONS FOR NURSING MANAGEMENT : Establishing an ICT-based nursing care system that considers the physical environment and communication infrastructure of healthcare institutions, user's digital health literacy, and user safety to effectively manage non-nursing care-related activities and undertake tasks that can be delegated may improve the quality of care for quarantined patients and reduce risk of cross-infection.

Yoo Hye Jin, Lee Hyeongsuk

2022-Nov-03

COVID-19, artificial intelligence, information technology, nursing care, patient isolation

General General

Computationally restoring the potency of a clinical antibody against SARS-CoV-2 Omicron subvariants.

In bioRxiv : the preprint server for biology

The COVID-19 pandemic has highlighted how viral variants that escape monoclonal antibodies can limit options to control an outbreak. With the emergence of the SARS-CoV-2 Omicron variant, many clinically used antibody drug products lost in vitro and in vivo potency, including AZD7442 and its constituent, AZD1061 [VanBlargan2022, Case2022]. Rapidly modifying such antibodies to restore efficacy to emerging variants is a compelling mitigation strategy. We therefore sought to computationally design an antibody that restores neutralization of BA.1 and BA.1.1 while simultaneously maintaining efficacy against SARS-CoV-2 B.1.617.2 (Delta), beginning from COV2-2130, the progenitor of AZD1061. Here we describe COV2-2130 derivatives that achieve this goal and provide a proof-of-concept for rapid antibody adaptation addressing escape variants. Our best antibody achieves potent and broad neutralization of BA.1, BA.1.1, BA.2, BA.2.12.1, BA.4, BA.5, and BA.5.5 Omicron subvariants, where the parental COV2-2130 suffers significant potency losses. This antibody also maintains potency against Delta and WA1/2020 strains and provides protection in vivo against the strains we tested, WA1/2020, BA.1.1, and BA.5. Because our design approach is computational-driven by high-performance computing-enabled simulation, machine learning, structural bioinformatics and multi-objective optimization algorithms-it can rapidly propose redesigned antibody candidates aiming to broadly target multiple escape variants and virus mutations known or predicted to enable escape.

Desautels Thomas A, Arrildt Kathryn T, Zemla Adam T, Lau Edmond Y, Zhu Fangqiang, Ricci Dante, Cronin Stephanie, Zost Seth J, Binshtein Elad, Scheaffer Suzanne M, Engdahl Taylor B, Chen Elaine, Goforth John W, Vashchenko Denis, Nguyen Sam, Weilhammer Dina R, Lo Jacky Kai-Yin, Rubinfeld Bonnee, Saada Edwin A, Weisenberger Tracy, Lee Tek-Hyung, Whitener Bradley, Case James B, Ladd Alexander, Silva Mary S, Haluska Rebecca M, Grzesiak Emilia A, Bates Thomas W, Petersen Brenden K, Thackray Larissa B, Segelke Brent W, Lillo Antonietta Maria, Sundaram Shivshankar, Diamond Michael S, Crowe James E, Carnahan Robert H, Faissol Daniel M

2022-Oct-24

General General

A novel deep learning-based method for COVID-19 pneumonia detection from CT images.

In BMC medical informatics and decision making ; h5-index 38.0

BACKGROUND : The sensitivity of RT-PCR in diagnosing COVID-19 is only 60-70%, and chest CT plays an indispensable role in the auxiliary diagnosis of COVID-19 pneumonia, but the results of CT imaging are highly dependent on professional radiologists.

AIMS : This study aimed to develop a deep learning model to assist radiologists in detecting COVID-19 pneumonia.

METHODS : The total study population was 437. The training dataset contained 26,477, 2468, and 8104 CT images of normal, CAP, and COVID-19, respectively. The validation dataset contained 14,076, 1028, and 3376 CT images of normal, CAP, and COVID-19 patients, respectively. The test set included 51 normal cases, 28 CAP patients, and 51 COVID-19 patients. We designed and trained a deep learning model to recognize normal, CAP, and COVID-19 patients based on U-Net and ResNet-50. Moreover, the diagnoses of the deep learning model were compared with different levels of radiologists.

RESULTS : In the test set, the sensitivity of the deep learning model in diagnosing normal cases, CAP, and COVID-19 patients was 98.03%, 89.28%, and 92.15%, respectively. The diagnostic accuracy of the deep learning model was 93.84%. In the validation set, the accuracy was 92.86%, which was better than that of two novice doctors (86.73% and 87.75%) and almost equal to that of two experts (94.90% and 93.88%). The AI model performed significantly better than all four radiologists in terms of time consumption (35 min vs. 75 min, 93 min, 79 min, and 82 min).

CONCLUSION : The AI model we obtained had strong decision-making ability, which could potentially assist doctors in detecting COVID-19 pneumonia.

Luo Ju, Sun Yuhao, Chi Jingshu, Liao Xin, Xu Canxia

2022-Nov-02

Artificial intelligence, COVID-19, CT image, Community acquired pneumonia, Deep learning

Public Health Public Health

An immune dysfunction score for stratification of patients with acute infection based on whole-blood gene expression.

In Science translational medicine ; h5-index 138.0

Dysregulated host responses to infection can lead to organ dysfunction and sepsis, causing millions of global deaths each year. To alleviate this burden, improved prognostication and biomarkers of response are urgently needed. We investigated the use of whole-blood transcriptomics for stratification of patients with severe infection by integrating data from 3149 samples from patients with sepsis due to community-acquired pneumonia or fecal peritonitis admitted to intensive care and healthy individuals into a gene expression reference map. We used this map to derive a quantitative sepsis response signature (SRSq) score reflective of immune dysfunction and predictive of clinical outcomes, which can be estimated using a 7- or 12-gene signature. Last, we built a machine learning framework, SepstratifieR, to deploy SRSq in adult and pediatric bacterial and viral sepsis, H1N1 influenza, and COVID-19, demonstrating clinically relevant stratification across diseases and revealing some of the physiological alterations linking immune dysregulation to mortality. Our method enables early identification of individuals with dysfunctional immune profiles, bringing us closer to precision medicine in infection.

Cano-Gamez Eddie, Burnham Katie L, Goh Cyndi, Allcock Alice, Malick Zunaira H, Overend Lauren, Kwok Andrew, Smith David A, Peters-Sengers Hessel, Antcliffe David, McKechnie Stuart, Scicluna Brendon P, van der Poll Tom, Gordon Anthony C, Hinds Charles J, Davenport Emma E, Knight Julian C, Webster Nigel, Galley Helen, Taylor Jane, Hall Sally, Addison Jenni, Roughton Sian, Tennant Heather, Guleri Achyut, Waddington Natalia, Arawwawala Dilshan, Durcan John, Short Alasdair, Swan Karen, Williams Sarah, Smolen Susan, Mitchell-Inwang Christine, Gordon Tony, Errington Emily, Templeton Maie, Venatesh Pyda, Ward Geraldine, McCauley Marie, Baudouin Simon, Higham Charley, Soar Jasmeet, Grier Sally, Hall Elaine, Brett Stephen, Kitson David, Wilson Robert, Mountford Laura, Moreno Juan, Hall Peter, Hewlett Jackie, McKechnie Stuart, Garrard Christopher, Millo Julian, Young Duncan, Hutton Paula, Parsons Penny, Smiths Alex, Faras-Arraya Roser, Soar Jasmeet, Raymode Parizade, Thompson Jonathan, Bowrey Sarah, Kazembe Sandra, Rich Natalie, Andreou Prem, Hales Dawn, Roberts Emma, Fletcher Simon, Rosbergen Melissa, Glister Georgina, Cuesta Jeronimo Moreno, Bion Julian, Millar Joanne, Perry Elsa Jane, Willis Heather, Mitchell Natalie, Ruel Sebastian, Carrera Ronald, Wilde Jude, Nilson Annette, Lees Sarah, Kapila Atul, Jacques Nicola, Atkinson Jane, Brown Abby, Prowse Heather, Krige Anton, Bland Martin, Bullock Lynne, Harrison Donna, Mills Gary, Humphreys John, Armitage Kelsey, Laha Shond, Baldwin Jacqueline, Walsh Angela, Doherty Nicola, Drage Stephen, Ortiz-Ruiz de Gordoa Laura, Lowes Sarah, Higham Charley, Walsh Helen, Calder Verity, Swan Catherine, Payne Heather, Higgins David, Andrews Sarah, Mappleback Sarah, Hind Charles, Garrard Chris, Watson D, McLees Eleanor, Purdy Alice, Stotz Martin, Ochelli-Okpue Adaeze, Bonner Stephen, Whitehead Iain, Hugil Keith, Goodridge Victoria, Cawthor Louisa, Kuper Martin, Pahary Sheik, Bellingan Geoffrey, Marshall Richard, Montgomery Hugh, Ryu Jung Hyun, Bercades Georgia, Boluda Susan, Bentley Andrew, Mccalman Katie, Jefferies Fiona, Knight Julian, Davenport Emma, Burnham Katie, Maugeri Narelle, Radhakrishnan Jayachandran, Mi Yuxin, Allcock Alice, Goh Cyndi

2022-Nov-02

General General

Application of Supervised Machine Learning Techniques to Forecast the COVID-19 U.S. Recession and Stock Market Crash.

In Computational economics

Machine learning (ML), a transformational technology, has been successfully applied to forecasting events down the road. This paper demonstrates that supervised ML techniques can be used in recession and stock market crash (more than 20% drawdown) forecasting. After learning from strictly past monthly data, ML algorithms detected the Covid-19 recession by December 2019, six months before the official NBER announcement. Moreover, ML algorithms foresaw the March 2020 S&P500 crash two months before it happened. The current labor market and housing are harbingers of a future U.S. recession (in 3 months). Financial factors have a bigger role to play in stock market crashes than economic factors. The labor market appears as a top-two feature in predicting both recessions and crashes. ML algorithms detect that the U.S. exited recession before December 2020, even though the official NBER announcement has not yet been made. They also do not anticipate a U.S. stock market crash before March 2021. ML methods have three times higher false discovery rates of recessions compared to crashes.

Malladi Rama K

2022-Oct-26

Financial econometrics, Forecasting, Machine learning, Recession, Stock market crash

Internal Medicine Internal Medicine

The Impact of COVID-19 on the Behaviors and Attitudes of Children and Adolescents: A Cross-Sectional Study.

In Cureus

Background and objective Over the past few decades, new infectious diseases have emerged, and these have played a key role in changing behavior and lifestyle in all age groups. More recently, with the emergence of the coronavirus disease 2019 (COVID-19) pandemic, governments around the world have made unprecedented efforts to contain the epidemic by implementing quarantine measures, social distancing, and isolating infected individuals. Social behavioral adaptations (e.g., social distancing, isolation, etc.) impact children's and adolescents' lifestyle activities and lead to increased incidence of psychosocial problems, worsening of preexisting mental illness, and fears of infection, uncertainty, isolation, and stress. In light of this, this study aimed to assess the impact of COVID-19 on the behaviors and lifestyles of the children and adolescent population of Pakistan. Methodology A cross-sectional study was conducted involving 323 children and adolescents by targeting parents of children and adolescents in the age group of 4-18 years living in Pakistan. The study was conducted from April 2021 to September 2021. A well-designed structured questionnaire was used to collect data about the sociodemographic profile, attitudes, and behavioral factors impacted by COVID-19 in children and adolescents. SPSS Statistics version 23 (IBM, Armonk, NY) was used to enter and analyze data. Results Parents or caregivers of a total of 189 male and 134 female children aged between four and 18 years took part in this study. During COVID-19, the consumption of fast food and fried foods by children and adolescents increased significantly. In this study, out of 323 participants, almost all (289, 89.5%) had increased their screen time significantly. Nearly half of the total individuals experienced the feeling of depression and loneliness during the pandemic. Additionally, some children and adolescents felt fearful when leaving home. COVID-19 lockdowns have led to many changes in children's and adolescents' lifestyle habits. They reduced physical contact with others due to the fear of transmission of COVID-19. Based on our findings, the pandemic and its containment strategies have adversely affected the behaviors, lifestyles, and attitudes of children and adolescents. Conclusion Governments around the world have imposed social distancing during the COVID-19 pandemic, leading to adverse short-term and long-term negative mental health issues such as unhappiness, fear, worry, irritability, depressive symptoms, anxiety, and post-traumatic stress disorder (PTSD). Interventions are needed to focus on building resilience in children and adolescents, addressing their fears and concerns through better communication, encouraging routine and physical activity, and taking measures to alleviate loneliness.

Annam Swetha, Fleming Maria F, Gulraiz Azouba, Zafar Muhammad Talha, Khan Saif, Oghomitse-Omene Princess T, Saleemuddin Sana, Patel Parth, Ahsan Zainab, Qavi Muhammad Saqlain S

2022-Sep

adolescent, child and adolescent psychiatry, child attitudes, child behaviour, covid-19, mental health

General General

The role of cryptocurrencies in predicting oil prices pre and during COVID-19 pandemic using machine learning.

In Annals of operations research

This study aims to explore the role of cryptocurrencies and the US dollar in predicting oil prices pre and during COVID-19 pandemic. The study uses three neural network models (i.e., Support vector machines, Multilayer Perceptron Neural Networks and Generalized regression neural networks (GRNN)) over the period from January 1, 2018, to July 5, 2021. Our results are threefold. First, our results indicate Bitcoin is the most influential in predicting oil prices during the bear and bull oil market before COVID-19 and during the downtrend during COVID-19. Second, COVID-19 variables became the most influential during the uptrend, especially the number of death cases. Third, our results also suggest that the most accurate model to predict the price of oil under the conditions of uncertainty that prevailed in the world during the bear and bull prices in the wake of COVID-19 is GRNN. Though the best prediction model under normal conditions before COVID-19 during an uptrend is SVM and during a downtrend is GRNN. Our results provide crucial evidence for investors, academics and policymakers, especially during global uncertainties.

Ibrahim Bassam A, Elamer Ahmed A, Abdou Hussein A

2022-Oct-28

Bitcoin, COVID-19, Crude oil, Cryptocurrencies, Machine learning, Neural networks

General General

Interpretable tourism demand forecasting with temporal fusion transformers amid COVID-19.

In Applied intelligence (Dordrecht, Netherlands)

An innovative ADE-TFT interpretable tourism demand forecasting model was proposed to address the issue of the insufficient interpretability of existing tourism demand forecasting. This model effectively optimizes the parameters of the Temporal Fusion Transformer (TFT) using an adaptive differential evolution algorithm (ADE). TFT is a brand-new attention-based deep learning model that excels in prediction research by fusing high-performance prediction with time-dynamic interpretable analysis. The TFT model can produce explicable predictions of tourism demand, including attention analysis of time steps and the ranking of input factors' relevance. While doing so, this study adds something unique to the literature on tourism by using historical tourism volume, monthly new confirmed cases of travel destinations, and big data from travel forums and search engines to increase the precision of forecasting tourist volume during the COVID-19 pandemic. The mood of travelers and the many subjects they spoke about throughout off-season and peak travel periods were examined using a convolutional neural network model. In addition, a novel technique for choosing keywords from Google Trends was suggested. In other words, the Latent Dirichlet Allocation topic model was used to categorize the major travel-related subjects of forum postings, after which the most relevant search terms for each topic were determined. According to the findings, it is possible to estimate tourism demand during the COVID-19 pandemic by combining quantitative and emotion-based characteristics.

Wu Binrong, Wang Lin, Zeng Yu-Rong

2022-Oct-27

COVID-19, Deep learning, Interpretable tourism demand forecasting, Text mining

General General

The m7G Modification Level and Immune Infiltration Characteristics in Patients with COVID-19.

In Journal of multidisciplinary healthcare

Purpose : The 7-methylguanosine (m7G)-related genes were used to identify the clinical severity and prognosis of patients with coronavirus disease 2019 (COVID-19) and to identify possible therapeutic targets.

Patients and Methods : The GSE157103 dataset provides the transcriptional spectrum and clinical information required to analyze the expression of m7G-related genes and the disease subtypes. R language was applied for immune infiltration analysis, functional enrichment analysis, and nomogram model construction.

Results : Most m7G-related genes were up-regulated in COVID-19 and were closely related to immune cell infiltration. Disease subtypes were grouped using a clustering algorithm. It was found that the m7G-cluster B was associated with higher immune infiltration, lower mechanical ventilation, lower intensive care unit (ICU) status, higher ventilator-free days, and lower m7G scores. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that differentially expressed genes (DEGs) between m7G-cluster A and B were enriched in viral infection and immune-related aspects, including COVID-19 infection; Th17, Th1, and Th2 cell differentiation, and human T-cell leukemia virus 1 infection. Finally, through machine learning, six disease characteristic genes, NUDT4B, IFIT5, LARP1, EIF4E, LSM1, and NUDT4, were screened and used to develop a nomogram model to estimate disease risk.

Conclusion : The expression of most m7G genes was higher in COVID-19 patients compared with that in non-COVID-19 patients. The m7G-cluster B showed higher immune infiltration and milder symptoms. The predictive nomogram based on the six m7G genes can be used to accurately assess risk.

Lu Lingling, Zheng Jiaolong, Liu Bang, Wu Haicong, Huang Jiaofeng, Wu Liqing, Li Dongliang

2022

7-methylguanosine, COVID-19, SARS-CoV-2, immune cells, nomogram, risk

General General

A comparison of Covid-19 cases and deaths in Turkey and in other countries.

In Network modeling and analysis in health informatics and bioinformatics

In this study, the characteristics of the Covid-19 pandemic in Turkey are examined in terms of the number of cases and deaths, and a characteristic prediction is made with an approach that employs artificial intelligence. The number of cases and deaths are estimated using the number of tests, the numbers of seriously ill and recovered patients as parameters. The machine learning methods used are linear regression, polynomial regression, support vector regression with different kernel functions, decision tree and artificial neural networks. The obtained results are compared by calculating the coefficient of determination (R 2), and the mean absolute percentage error (MAPE) values. When R 2 and MAPE values are compared, it is seen that the optimal results for cases in Turkey are obtained with the decision tree, for deaths with polynomial regression method. The results reached for the United States of America and Russia are similar and the optimal results are obtained by polynomial regression. However, while the optimal results are obtained by neural networks in the Indian data, linear regression for the cases in the Brazilian data, neural network for the deaths, decision tree for the cases in France, polynomial regression for the deaths, neural network for the cases in the UK data and decision tree for the deaths are the methods that produced the optimal results. These results also give an idea about the similarities and differences of country characteristics.

Çağlar Oğuzhan, Özen Figen

2022

Artificial neural network, Covid-19, Decision tree, Linear regression, Polynomial regression, Support vector regression

General General

A robust semantic lung segmentation study for CNN-based COVID-19 diagnosis.

In Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society

This paper aims to diagnose COVID-19 by using Chest X-Ray (CXR) scan images in a deep learning-based system. First of all, COVID-19 Chest X-Ray Dataset is used to segment the lung parts in CXR images semantically. DeepLabV3+ architecture is trained by using the masks of the lung parts in this dataset. The trained architecture is then fed with images in the COVID-19 Radiography Database. In order to improve the output images, some image preprocessing steps are applied. As a result, lung regions are successfully segmented from CXR images. The next step is feature extraction and classification. While features are extracted with modified AlexNet (mAlexNet), Support Vector Machine (SVM) is used for classification. As a result, 3-class data consisting of Normal, Viral Pneumonia and COVID-19 class are classified with 99.8% success. Classification results show that the proposed method is superior to previous state-of-the-art methods.

Aslan Muhammet Fatih

2022-Dec-15

AlexNet, COVID-19, Convolutional neural networks, DeepLabV3+, Semantic segmentation, Support vector machine

General General

Predictive models for COVID-19 detection using routine blood tests and machine learning.

In Heliyon

The problem of accurate, fast, and inexpensive COVID-19 tests has been urgent till now. Standard COVID-19 tests need high-cost reagents and specialized laboratories with high safety requirements, are time-consuming. Data of routine blood tests as a base of SARS-CoV-2 invasion detection allows using the most practical medicine facilities. But blood tests give general information about a patient's state, which is not directly associated with COVID-19. COVID-19-specific features should be selected from the list of standard blood characteristics, and decision-making software based on appropriate clinical data should be created. This review describes the abilities to develop predictive models for COVID-19 detection using routine blood tests and machine learning.

Kistenev Yury V, Vrazhnov Denis A, Shnaider Ekaterina E, Zuhayri Hala

2022-Oct

Blood tests, COVID-19, Machine learning

General General

Defending against adversarial attacks on Covid-19 classifier: A denoiser-based approach.

In Heliyon

Covid-19 has posed a serious threat to the existence of the human race. Early detection of the virus is vital to effectively containing the virus and treating the patients. Profound testing methods such as the Real-time reverse transcription-polymerase chain reaction (RT-PCR) test and the Rapid Antigen Test (RAT) are being used for detection, but they have their limitations. The need for early detection has led researchers to explore other testing techniques. Deep Neural Network (DNN) models have shown high potential in medical image classification and various models have been built by researchers which exhibit high accuracy for the task of Covid-19 detection using chest X-ray images. However, it is proven that DNNs are inherently susceptible to adversarial inputs, which can compromise the results of the models. In this paper, the adversarial robustness of such Covid-19 classifiers is evaluated by performing common adversarial attacks, which include the Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD). Using these attacks, it is found that the accuracy of the models for Covid-19 samples decreases drastically. In the medical domain, adversarial training is the most widely explored technique to defend against adversarial attacks. However, using this technique requires replacing the original model and retraining it by including adversarial samples. Another defensive technique, High-Level Representation Guided Denoiser (HGD), overcomes this limitation by employing an adversarial filter which is also transferable across models. Moreover, the HGD architecture, being suitable for high-resolution images, makes it a good candidate for medical image applications. In this paper, the HGD architecture has been evaluated as a potential defensive technique for the task of medical image analysis. Experiments carried out show an increased accuracy of up to 82% in the white box setting. However, in the black box setting, the defense completely fails to defend against adversarial samples.

Kansal Keshav, Krishna P Sai, Jain Parshva B, R Surya, Honnavalli Prasad, Eswaran Sivaraman

2022-Oct

Adversarial attacks, Deep neural network, Denoiser, FGSM, HGD, Machine learning, PGD

General General

Masked Facial Recognition in Security Systems Using Transfer Learning.

In SN computer science

The COVID-19 is a crisis of unprecedented magnitude, which has resulted in countless casualties and security troubles. In view of recent events of corona virus people are required to wear face masks to protect themselves from getting infected. As a result, a good portion of face (nose and mouth) is hidden by the mask and hence the facial recognition becomes difficult. Many organizations use facial recognition as a means of authentication. Researchers focus on developing rapid and efficient solutions to deal with the ongoing coronavirus pandemic by coming up with suggestions for handling the facial recognition problem. This research paper aims to identify the person, while the face is covered with a facial mask with only eyes and forehead being exposed. The first step involves marking the facial region. Next, using the data set, we will implement an object detection model YOLOv3 to identify unmasked and masked faces. The YOLO v3 object detection model is the best performing model with a detection time of 0.012 s, F1 score of 0.90 and mAP score of 0.92. Experimental results on Real-World Masked-Face-Data set show high recognition performance.

Ramgopal M, Roopesh M Sai, Chowdary M Veeranna, Madhav M, Shanmuga K

2023

Convolutional neural networks (CNN), Face recognition, Machine learning (ML), Object detection model, Transfer learning model

Cardiology Cardiology

The Recent Advances of Mobile Healthcare in Cardiology Practice.

In Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH

Background : Digitalization of healthcare led to the optimization of monitoring, diagnostics, and treatment of the range of disorders. Taking into account recent situation with COVID-19 pandemics, digital technologies allowed to improve management of viral infections via remote monitoring and diagnostics of infected patients. Up to date, various mobile health applications (apps) have been proposed, including apps for the patients diagnosed with cardiovascular pathologies.

Objective : The presented review aimed at the analyses of a range of mHealth solutions used to improve primary cardiac care. In addition, we studied the factors driving and hindering the wide introduction of mHealth services in the clinics.

Methods : The work was based on the guidelines of the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement. The publication search was carried out using PubMed, Web of Science, Cochrane Library, Scopus, and Google Scholar databases. Studies published during the period from 2014 until January 2022 were selected for the analysis. The evaluation of risk of bias for the included studies was conducted using the Cochrane Collaboration Risk of Bias tool.

Results and Discussion : An overall 5513 studies were assessed for eligibility after which 39 studies were included.. The main trend in the mobile health for cardiological applications is the use of different types of wearable devices and Artificial Intelligence-platforms. In fact, mobile technology allows remotely to monitor, interpret, and analyze biomedical data collected from the patient.

Conclusion : The results of this literature search demonstrated that patients diagnosed with cardiovascular disorders can potentially benefit from the application of mHealth in cardiology. However, despite the proven advantages of mHealth for cardiology, there are many challenges and concerns regarding effectiveness, safety, reliability and the lack of official regulation and guidelines from official organizations. Such issues require solutions and further work towards a wide implementation of mHealth technologies in cardiac practice.

Kulbayeva Shynar, Tazhibayeva Karlygash, Seiduanova Laura, Smagulova Indira, Mussina Aiman, Tanabayeva Shynar, Fakhradiyev Ildar, Saliev Timur

2022-Sep

cardiology, mobile app, mobile applications, telemedicine

General General

Digital phenotyping for classification of anxiety severity during COVID-19.

In Frontiers in digital health

COVID-19 has led to an increase in anxiety among Canadians. Canadian Perspectives Survey Series (CPSS) is a dataset created by Statistics Canada to monitor the effects of COVID-19 among Canadians. Survey data were collected to evaluate health and health-related behaviours. This work evaluates CPSS2 and CPSS4, which were collected in May and July of 2020, respectively. The survey data consist of up to 102 questions. This work proposes the use of the survey data characteristics to identify the level of anxiety within the Canadian population during the first- and second-phases of COVID-19 and is validated by using the General Anxiety Disorder (GAD)-7 questionnaire. Minimum redundancy maximum relevance (mRMR) is applied to select the top features to represent user anxiety, and support vector machine (SVM) is used to classify the separation of anxiety severity. We employ SVM for binary classification with 10-fold cross validation to separate the labels of Minimal and Severe anxiety to achieve an overall accuracy of 94.77 ± 0.13 % and 97.35 ± 0.11 % for CPSS2 and CPSS4, respectively. After analysis, we compared the results of the first and second phases of COVID-19 and determined a subset of the features that could be represented as pseudo passive (PP) data. The accurate classification provides a proxy on the potential onsets of anxiety to provide tailored interventions. Future works can augment the proposed PP data for carrying out a more detailed digital phenotyping.

Nguyen Binh, Ivanov Martin, Bhat Venkat, Krishnan Sri

2022

COVID-19, anxiety, digital phenotyping, machine learning, mental health

General General

Learning effective embedding for automated COVID-19 prediction from chest X-ray images.

In Multimedia systems

The pandemic that the SARS-CoV-2 originated in 2019 is continuing to cause serious havoc on the global population's health, economy, and livelihood. A critical way to suppress and restrain this pandemic is the early detection of COVID-19, which will help to control the virus. Chest X-rays are one of the more straightforward ways to detect the COVID-19 virus compared to the standard methods like CT scans and RT-PCR diagnosis, which are very complex, expensive, and take much time. Our research on various papers shows that the currently researchers are actively working for an efficient Deep Learning model to produce an unbiased detection of COVID-19 through chest X-ray images. In this work, we propose a novel convolution neural network model based on supervised classification that simultaneously computes identification and verification loss. We adopt a transfer learning approach using pretrained models trained on imagenet dataset such as Alex Net and VGG16 as back-bone models and use data augmentation techniques to solve class imbalance and boost the classifier's performance. Finally, our proposed classifier architecture model ensures unbiased and high accuracy results, outperforming existing deep learning models for COVID-19 detection from chest X-ray images producing State of the Art performance. It shows strong and robust performance and proves to be easily deployable and scalable, therefore increasing the efficiency of analyzing chest X-ray images with high accuracy in detection of Coronavirus.

T N Sree Ganesh, Satish Rishi, Sridhar Rajeswari

2022-Oct-26

AlexNet, COVID-19 prediction, Convolution neural network, Medical image classification, Multitask learning, Siamese neural network, Transfer learning, VGG16

General General

Modelling and forecasting new cases of Covid-19 in Nigeria: Comparison of regression, ARIMA and machine learning models.

In Scientific African

Covid-19 remains a global pandemic threatening hundreds of countries in the world. The impact of Covid-19 has been felt in almost every aspect of life and it has introduced globally, a new normal of livelihood. This global pandemic has triggered unparalleled global health and economic crisis. Therefore, modelling and forecasting the dynamics of this pandemic is very crucial as it will help in decision making and strategic planning. Nigeria as the most populous country in Africa and most populous black nation in the world has been adversely affected by Covid-19 pandemic. This study models and compares forecasting performance of regression, ARIMA and Machine Learning models in predicting new cases of Covid-19 in Nigeria. The study obtained data on daily new cases of Covid-19 in Nigeria between 27th February, 2020 and 30th November, 2021. Graphical analysis showed that Nigeria had witnessed three waves of Covid-19 with the first wave between 27th February, 2020 and 23rd October, 2020, the second wave between 24th October, 2020 and 20th June, 2021 and the third wave between 21st June, 2021 and 30th November, 2021.The second wave recorded the highest spikes in new cases compared to the first wave and third wave. Result reveals that in terms of forecasting performance, inverse regression model outperformed other regression models considered as it shows lowest RMSE of 0.4130 compared with other regression models. Also, the ARIMA (4, 1, 4) outperformed other ARIMA models as it reveals the highest R2 of 0.856 (85.6%), least RMSE (0.6364), AIC (-8.6024) and BIC (-8.5299). Result reveals that Fine tree which is one of the Machine Learning models is more reliable in forecasting new cases of Covid-19 in Nigeria compared to other models as Fine tree gave the highest R2 of 0.90 (90.0%) and least RMSE of 0.22165. Result of 15 days forecasting indicates that Covid-19 pandemic is not over yet in Nigeria as new cases of Covid-19 is projected to increase on 15/12/2021 with predicted new cases of 988 compared with that of 14/12/2021, where only 729 new cases was predicted. This therefore emphasizes the need to strengthen and maintain the existing Covid-19 preventive measures in Nigeria.

Busari S I, Samson T K

2022-Nov

ARIMA, Forecasting, Machine learning, Time series

General General

Ultrasensitive NO Sensor Based on a Nickel Single-Atom Electrocatalyst for Preliminary Screening of COVID-19.

In ACS sensors

A new coronavirus, SARS-CoV-2, has caused the coronavirus disease-2019 (COVID-19) epidemic. A rapid and economical method for preliminary screening of COVID-19 may help to control the COVID-19 pandemic. Here, we report a nickel single-atom electrocatalyst that can be printed on a paper-printing sensor for preliminary screening of COVID-19 suspects by efficient detection of fractional exhaled nitric oxide (FeNO). The FeNO value is confirmed to be related to COVID-19 in our exploratory clinical study, and a machine learning model that can accurately classify healthy subjects and COVID-19 patients is established based on FeNO and other features. The nickel single-atom electrocatalyst consists of a single nickel atom with N2O2 coordination embedded in porous acetylene black (named Ni-N2O2/AB). A paper-printed sensor was fabricated with the material and showed ultrasensitive response to NO in the range of 0.3-180 ppb. This ultrasensitive sensor could be applied to preliminary screening of COVID-19 in everyday life.

Zhou Wei, Tan Yi, Ma Jing, Wang Xiao, Yang Li, Li Zhen, Liu Chengcheng, Wu Hao, Sun Lei, Deng Weiqiao

2022-Oct-31

COVID-19, Ni-N2O2/AB, mini-exhaled nitric oxide sensor, preliminary screening, single-atom catalysts

General General

Artificial intelligence-based approaches for traditional fermented alcoholic beverages' development: review and prospect.

In Critical reviews in food science and nutrition ; h5-index 70.0

Traditional fermented alcoholic beverages (TFABs) have gained widespread acceptance and enjoyed great popularity for centuries. COVID-19 pandemics lead to the surge in health demand for diet, thus TFABs once again attract increased focus for the health benefits. Though the production technology is quite mature, food companies and research institutions are looking for transformative innovation in TFABs to make healthy, nutritious offerings that give a competitive advantage in current beverage market. The implementation of intelligent platforms enables companies and researchers to gather, store and analyze data in a more convenient way. The development of data collection methods contributed to the big data environment of TFABs, providing a fresh perspective that helps brewers to observe and improve the production steps. Among data analytical tools, Artificial Intelligence (AI) is considered to be one of the most promising methodological approaches for big data analytics and decision-making of automated production, and machine learning (ML) is an important method to fulfill the goal. This review describes the development trends and challenges of TFABs in big data era and summarize the application of AI-based methods in TFABs. Finally, we provide perspectives on the potential research directions of new frontiers in application of AI approaches in the supply chain of TFABs.

Yu Huakun, Liu Shuangping, Qin Hui, Zhou Zhilei, Zhao Hongyuan, Zhang Suyi, Mao Jian

2022-Oct-31

Traditional fermented alcoholic beverages, artificial intelligence, big data, fermentation regulation, microbial community

Radiology Radiology

Severity detection of COVID-19 infection with machine learning of clinical records and CT images.

In Technology and health care : official journal of the European Society for Engineering and Medicine

BACKGROUND : Coronavirus disease 2019 (COVID-19) is a deadly viral infection spreading rapidly around the world since its outbreak in 2019. In the worst case a patient's organ may fail leading to death. Therefore, early diagnosis is crucial to provide patients with adequate and effective treatment.

OBJECTIVE : This paper aims to build machine learning prediction models to automatically diagnose COVID-19 severity with clinical and computed tomography (CT) radiomics features.

METHOD : P-V-Net was used to segment the lung parenchyma and then radiomics was used to extract CT radiomics features from the segmented lung parenchyma regions. Over-sampling, under-sampling, and a combination of over- and under-sampling methods were used to solve the data imbalance problem. RandomForest was used to screen out the optimal number of features. Eight different machine learning classification algorithms were used to analyze the data.

RESULTS : The experimental results showed that the COVID-19 mild-severe prediction model trained with clinical and CT radiomics features had the best prediction results. The accuracy of the GBDT classifier was 0.931, the ROUAUC 0.942, and the AUCPRC 0.694, which indicated it was better than other classifiers.

CONCLUSION : This study can help clinicians identify patients at risk of severe COVID-19 deterioration early on and provide some treatment for these patients as soon as possible. It can also assist physicians in prognostic efficacy assessment and decision making.

Zhu Fubao, Zhu Zelin, Zhang Yijun, Zhu Hanlei, Gao Zhengyuan, Liu Xiaoman, Zhou Guanbin, Xu Yan, Shan Fei

2022-Oct-21

COVID-19, CT radiomics features, Severity detection, clinical features, imbalance classification

Ophthalmology Ophthalmology

Acceptance and Perception of Artificial Intelligence Usability in Eye Care (APPRAISE) for Ophthalmologists: A Multinational Perspective.

In Frontiers in medicine

Background : Many artificial intelligence (AI) studies have focused on development of AI models, novel techniques, and reporting guidelines. However, little is understood about clinicians' perspectives of AI applications in medical fields including ophthalmology, particularly in light of recent regulatory guidelines. The aim for this study was to evaluate the perspectives of ophthalmologists regarding AI in 4 major eye conditions: diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD) and cataract.

Methods : This was a multi-national survey of ophthalmologists between March 1st, 2020 to February 29th, 2021 disseminated via the major global ophthalmology societies. The survey was designed based on microsystem, mesosystem and macrosystem questions, and the software as a medical device (SaMD) regulatory framework chaired by the Food and Drug Administration (FDA). Factors associated with AI adoption for ophthalmology analyzed with multivariable logistic regression random forest machine learning.

Results : One thousand one hundred seventy-six ophthalmologists from 70 countries participated with a response rate ranging from 78.8 to 85.8% per question. Ophthalmologists were more willing to use AI as clinical assistive tools (88.1%, n = 890/1,010) especially those with over 20 years' experience (OR 3.70, 95% CI: 1.10-12.5, p = 0.035), as compared to clinical decision support tools (78.8%, n = 796/1,010) or diagnostic tools (64.5%, n = 651). A majority of Ophthalmologists felt that AI is most relevant to DR (78.2%), followed by glaucoma (70.7%), AMD (66.8%), and cataract (51.4%) detection. Many participants were confident their roles will not be replaced (68.2%, n = 632/927), and felt COVID-19 catalyzed willingness to adopt AI (80.9%, n = 750/927). Common barriers to implementation include medical liability from errors (72.5%, n = 672/927) whereas enablers include improving access (94.5%, n = 876/927). Machine learning modeling predicted acceptance from participant demographics with moderate to high accuracy, and area under the receiver operating curves of 0.63-0.83.

Conclusion : Ophthalmologists are receptive to adopting AI as assistive tools for DR, glaucoma, and AMD. Furthermore, ML is a useful method that can be applied to evaluate predictive factors on clinical qualitative questionnaires. This study outlines actionable insights for future research and facilitation interventions to drive adoption and operationalization of AI tools for Ophthalmology.

Gunasekeran Dinesh V, Zheng Feihui, Lim Gilbert Y S, Chong Crystal C Y, Zhang Shihao, Ng Wei Yan, Keel Stuart, Xiang Yifan, Park Ki Ho, Park Sang Jun, Chandra Aman, Wu Lihteh, Campbel J Peter, Lee Aaron Y, Keane Pearse A, Denniston Alastair, Lam Dennis S C, Fung Adrian T, Chan Paul R V, Sadda SriniVas R, Loewenstein Anat, Grzybowski Andrzej, Fong Kenneth C S, Wu Wei-Chi, Bachmann Lucas M, Zhang Xiulan, Yam Jason C, Cheung Carol Y, Pongsachareonnont Pear, Ruamviboonsuk Paisan, Raman Rajiv, Sakamoto Taiji, Habash Ranya, Girard Michael, Milea Dan, Ang Marcus, Tan Gavin S W, Schmetterer Leopold, Cheng Ching-Yu, Lamoureux Ecosse, Lin Haotian, van Wijngaarden Peter, Wong Tien Y, Ting Daniel S W

2022

artificial intelligence (AI), implementation, ophthalmology, regulation, translation

Public Health Public Health

A comprehensive review on variants of SARS-CoVs-2: Challenges, solutions and open issues.

In Computer communications

SARS-CoV-2 is an infected disease caused by one of the variants of Coronavirus which emerged in December 2019. It is declared a pandemic by WHO in March 2020. COVID-19 outbreak has put the world on a halt and is a major threat to the public health system. It has shattered the world with its effects on different areas as the pandemic hit the world in a number of waves with different variants and mutations. Each variant and mutation have different transmission and infection rates in the human population. More than 609 million people have tested positive and more than 6.5 million people have died due to this disease as per 14th September 2022. Despite of numerous efforts, precautions and vaccination the infection has grown rapidly in the world. In this paper, we aim to give a holistic overview of COVID-19 its variants, game theory perspective, effects on the different social and economic areas, diagnostic advancements, treatment methods. A taxonomy is made for the proper insight of the work demonstrated in the paper. Finally, we discuss the open issues associated with COVID-19 in different fields and futuristic research trends in the area. The main aim of the paper is to provide comprehensive literature that covers all the areas and provide an expert understanding of the COVID-19 techniques and potentially be further utilized to combat the outbreak of COVID-19.

Deepanshi Budhiraja, Ishan Garg, Deepak Kumar, Neeraj Sharma

2022-Oct-26

COVID-19, Deep learning, Game theory, Machine learning, SARS-CoV-2, Variants

General General

Ensemble hybrid model for Hindi COVID-19 text classification with metaheuristic optimization algorithm.

In Multimedia tools and applications

A SARS-CoV-2 virus has spread around the globe since March 2020. Millions of people infected worldwide with coronavirus. People from every country expressed their sentiments about coronavirus on social media. The aim of this work is to determine the general public opinion of Indian Twitter users about coronavirus. The Hindi tweets posted about COVID-19 is used as input data for sentiment analysis. The natural language processing is applied on input data for feature extraction. Further, the optimal features are selected from the pre-processed data using the metaheuristic based Grey wolf optimization technique. Finally, a hybrid of convolution neural network(CNN) and a long short-term memory (LSTM) model pair is employed to categorize the sentiments as positive, negative, and neutral. The outcome of the proposed model is compared with other machine learning techniques, namely, Random Forest, Decision Tree, K-Nearest Neighbor, Naive Bayes, Support vector machine (SVM), CNN, LSTM, LSTM-CNN, and CNN-LSTM. The highest accuracy of 87.75%, 88.41%, 87.89%, 85.54%, 89.11%, 91.46%, 88.72%, 91.54%, and 92.34% is obtained by Random Forest, Decision Tree, K-Nearest Neighbor, Naive Bayes, SVM, CNN, LSTM, LSTM-CNN, and CNN-LSTM, respectively. The proposed ensemble hybrid model gives the highest 95.54%, 91.44%, 89.63%, and 90.87% classification accuracy, precision, recall, and F-score, respectively.

Jain Vipin, Kashyap Kanchan Lata

2022-Oct-24

COVID-19, Deep learning, Ensemble learning, Grey wolf, Optimization, Sentiment

General General

A hybrid LBP-DCNN based feature extraction method in YOLO: An application for masked face and social distance detection.

In Multimedia tools and applications

COVID-19 is an ongoing pandemic and the WHO recommends at least one-meter social distance, and the use of medical face masks to slow the disease's transmission. This paper proposes an automated approach for detecting social distance and face masks. Thus, it aims to help the reduction of diseases transferred by respiratory droplets such as COVID-19. For this system, a two-cascaded YOLO is used. The first cascade detects humans in the environment and computes the social distance between them. Then, the second cascade detects human faces with or without a mask. Finally, red bounding boxes encircle the people's images that did not follow the rules. Also, in this paper, we propose a two-part feature extraction approach used with YOLO. The first part of the proposed feature extraction method extracts general features using the transfer learning approach. The second part extracts better features specific to the current task using the LBP layer and classification layers. The best average precision for the human detection task was obtained as 66% using Resnet50 in YOLO. The best average precision for the mask detection was obtained as 95% using Darknet19+LBP with YOLO. Also, another popular object detection network, Faster R-CNN, have been used for comparison purpose. The proposed system performed better than the literature in human and mask detection tasks.

Oztel Ismail, Yolcu Oztel Gozde, Akgun Devrim

2022-Oct-21

Covid-19, Deep learning, Face mask detection, Human detection

General General

From luxury to necessity: Progress of touchless interaction technology.

In Technology in society

Touchless Technology is facilitating the move to Zero User Interface(UI) propelled by the COVID-19 pandemic which has accelerated the use of this technology due to hygiene requirements. Zero UI can be defined as a controlled interface that enables user interaction with technology through voice, gestures, hand interaction, eye tracking, and biometrics such as facial recognition and contactless fingerprints. Smart devices, IoT sensors, smart appliances, smart TVs, smart assistants and consumer robotics are predominant examples of devices in which Zero UI is becoming increasingly adopted. These control interfaces include natural interaction modes such as voice or gestures. Touchscreens and shared devices such as kiosks, self-service counters and interactive displays are present in our everyday lives. Each of these interactions however is a concern for consumers in a post-COVID-19 world where hygiene is of utmost importance. The one-stop solution to hygienic interactions includes touchless technology such as voice control, remote mobile screen take over, biometric, and gesture control as Zero User interfaces. With the breakthroughs in image recognition and natural language processing, powered by advanced computer vision and machine learning, "Zero UI" is becoming a new normal. This paper is focusing on the progress of the touchless interaction technology during the COVID-19 pandemic, which actually accelerated development in this concept and moved it from being a luxury to a life necessity.

Iqbal Muhammad Zahid, Campbell Abraham G

2021-Nov

Contactless, Gestures, Human computer interaction, Touchless interaction, Touchless technology, Zero UI, Zero touch

General General

Boosted crow search algorithm for handling multi-threshold image problems with application to X-ray images of COVID-19.

In Expert systems with applications

COVID-19 is pervasive and threatens the safety of people around the world. Therefore, now, a method is needed to diagnose COVID-19 accurately. The identification of COVID-19 by X-ray images is a common method. The target area is extracted from the X-ray images by image segmentation to improve classification efficiency and help doctors make a diagnosis. In this paper, we propose an improved crow search algorithm (CSA) based on variable neighborhood descent (VND) and information exchange mutation (IEM) strategies, called VMCSA. The original CSA quickly falls into the local optimum, and the possibility of finding the best solution is significantly reduced. Therefore, to help the algorithm avoid falling into local optimality and improve the global search capability of the algorithm, we introduce VND and IEM into CSA. Comparative experiments are conducted at CEC2014 and CEC'21 to demonstrate the better performance of the proposed algorithm in optimization. We also apply the proposed algorithm to multi-level thresholding image segmentation using Renyi's entropy as the objective function to find the optimal threshold, where we construct 2-D histograms with grayscale images and non-local mean images and maximize the Renyi's entropy on top of the 2-D histogram. The proposed segmentation method is evaluated on X-ray images of COVID-19 and compared with some algorithms. VMCSA has a significant advantage in segmentation results and obtains better robustness than other algorithms. The available extra info can be found at https://github.com/1234zsw/VMCSA.

Zhao Songwei, Wang Pengjun, Heidari Ali Asghar, Zhao Xuehua, Chen Huiling

2023-Mar-01

2D histogram, COVID-19, Crow search algorithm, Multi-threshold image segmentation, Optimization, Renyi’s entropy

Radiology Radiology

Application of artificial intelligence in diagnosing COVID-19 disease symptoms on chest X-rays: A systematic review.

In International journal of medical sciences

This systematic review focuses on using artificial intelligence (AI) to detect COVID-19 infection with the help of X-ray images. Methodology: In January 2022, the authors searched PubMed, Embase and Scopus using specific medical subject headings terms and filters. All articles were independently reviewed by two reviewers. All conflicts resulting from a misunderstanding were resolved by a third independent researcher. After assessing abstracts and article usefulness, eliminating repetitions and applying inclusion and exclusion criteria, six studies were found to be qualified for this study. Results: The findings from individual studies differed due to the various approaches of the authors. Sensitivity was 72.59%-100%, specificity was 79%-99.9%, precision was 74.74%-98.7%, accuracy was 76.18%-99.81%, and the area under the curve was 95.24%-97.7%. Conclusion: AI computational models used to assess chest X-rays in the process of diagnosing COVID-19 should achieve sufficiently high sensitivity and specificity. Their results and performance should be repeatable to make them dependable for clinicians. Moreover, these additional diagnostic tools should be more affordable and faster than the currently available procedures. The performance and calculations of AI-based systems should take clinical data into account.

Kufel Jakub, Bargieł Katarzyna, Koźlik Maciej, Czogalik Łukasz, Dudek Piotr, Jaworski Aleksander, Cebula Maciej, Gruszczyńska Katarzyna

2022

COVID-19, artificial intelligence, chest X-rays, convolutional neural network, diagnostic imaging

General General

Towards Smart Diagnostic Methods for COVID-19: Review of Deep Learning for Medical Imaging.

In IPEM-translation

The infectious disease known as COVID-19 has spread dramatically all over the world since December 2019. The fast diagnosis and isolation of infected patients are key factors in slowing down the spread of this virus and better management of the pandemic. Although the CT and X-ray modalities are commonly used for the diagnosis of COVID-19, identifying COVID-19 patients from medical images is a time-consuming and error-prone task. Artificial intelligence has shown to have great potential to speed up and optimize the prognosis and diagnosis process of COVID-19. Herein, we review publications on the application of deep learning (DL) techniques for diagnostics of patients with COVID-19 using CT and X-ray chest images for a period from January 2020 to October 2021. Our review focuses solely on peer-reviewed, well-documented articles. It provides a comprehensive summary of the technical details of models developed in these articles and discusses the challenges in the smart diagnosis of COVID-19 using DL techniques. Based on these challenges, it seems that the effectiveness of the developed models in clinical use needs to be further investigated. This review provides some recommendations to help researchers develop more accurate prediction models.

Moghaddam Marjan Jalali, Ghavipour Mina

2022-Oct-26

Artificial Intelligence, CT-Scan, Classification, Segmentation, X-ray

General General

A deep transfer learning-based convolution neural network model for COVID-19 detection using Computed tomography scan images for medical applications.

In Advances in engineering software (Barking, London, England : 1992)

The Coronavirus (COVID-19) has become a critical and extreme epidemic because of its international dissemination. COVID-19 is the world's most serious health, economic, and survival danger. This disease affects not only a single country but the entire planet due to this infectious disease. Illnesses of Covid-19 spread at a much faster rate than usual influenza cases. Because of its high transmissibility and early diagnosis, it isn't easy to manage COVID-19. The popularly used RT-PCR method for COVID-19 disease diagnosis may provide false negatives. COVID-19 can be detected non-invasively using medical imaging procedures such as chest CT and chest x-ray. Deep learning is the most effective machine learning approach for examining a considerable quantity of chest computed tomography (CT) pictures that can significantly affect Covid-19 screening. Convolutional neural network (CNN) is one of the most popular deep learning techniques right now, and its gaining traction due to its potential to transform several spheres of human life. This research aims to develop conceptual transfer learning enhanced CNN framework models for detecting COVID-19 with CT scan images. Though with minimal datasets, these techniques were demonstrated to be effective in detecting the presence of COVID-19. This proposed research looks into several deep transfer learning-based CNN approaches for detecting the presence of COVID-19 in chest CT images.VGG16, VGG19, Densenet121, InceptionV3, Xception, and Resnet50 are the foundation models used in this work. Each model's performance was evaluated using a confusion matrix and various performance measures such as accuracy, recall, precision, f1-score, loss, and ROC. The VGG16 model performed much better than the other models in this study (98.00 % accuracy). Promising outcomes from experiments have revealed the merits of the proposed model for detecting and monitoring COVID-19 patients. This could help practitioners and academics create a tool to help minimal health professionals decide on the best course of therapy.

Kathamuthu Nirmala Devi, Subramaniam Shanthi, Le Q H, Muthusamy Suresh, Panchal Hitesh, Sundararajan Suma Christal Mary, Alruabie Ali Jawad, Zahra Musaddak Maher Abdul

2022-Oct-24

CNN, Covid-19, Deep Learning, DenseNet121, InceptionV3, ResNet-50, Transfer learning, VGG16, VGG19

Radiology Radiology

Deep-learning-based hepatic fat assessment (DeHFt) on non-contrast chest CT and its association with disease severity in COVID-19 infections: A multi-site retrospective study.

In EBioMedicine

BACKGROUND : Hepatic steatosis (HS) identified on CT may provide an integrated cardiometabolic and COVID-19 risk assessment. This study presents a deep-learning-based hepatic fat assessment (DeHFt) pipeline for (a) more standardised measurements and (b) investigating the association between HS (liver-to-spleen attenuation ratio <1 in CT) and COVID-19 infections severity, wherein severity is defined as requiring invasive mechanical ventilation, extracorporeal membrane oxygenation, death.

METHODS : DeHFt comprises two steps. First, a deep-learning-based segmentation model (3D residual-UNet) is trained (N = 80) to segment the liver and spleen. Second, CT attenuation is estimated using slice-based and volumetric-based methods. DeHFt-based mean liver and liver-to-spleen attenuation are compared with an expert's ROI-based measurements. We further obtained the liver-to-spleen attenuation ratio in a large multi-site cohort of patients with COVID-19 infections (D1, N = 805; D2, N = 1917; D3, N = 169) using the DeHFt pipeline and investigated the association between HS and COVID-19 infections severity.

FINDINGS : The DeHFt pipeline achieved a dice coefficient of 0.95, 95% CI [0.93-0.96] on the independent validation cohort (N = 49). The automated slice-based and volumetric-based liver and liver-to-spleen attenuation estimations strongly correlated with expert's measurement. In the COVID-19 cohorts, severe infections had a higher proportion of patients with HS than non-severe infections (pooled OR = 1.50, 95% CI [1.20-1.88], P < .001).

INTERPRETATION : The DeHFt pipeline enabled accurate segmentation of liver and spleen on non-contrast CTs and automated estimation of liver and liver-to-spleen attenuation ratio. In three cohorts of patients with COVID-19 infections (N = 2891), HS was associated with disease severity. Pending validation, DeHFt provides an automated CT-based metabolic risk assessment.

FUNDING : For a full list of funding bodies, please see the Acknowledgements.

Modanwal Gourav, Al-Kindi Sadeer, Walker Jonathan, Dhamdhere Rohan, Yuan Lei, Ji Mengyao, Lu Cheng, Fu Pingfu, Rajagopalan Sanjay, Madabhushi Anant

2022-Oct-26

COVID-19, Hepatic steatosis, NAFLD

General General

Multisite Evaluation of Prediction Models for Emergency Department Crowding Before and During the COVID-19 Pandemic.

In Journal of the American Medical Informatics Association : JAMIA

OBJECTIVE : To develop a machine learning framework to forecast emergency department (ED) crowding and to evaluate model performance under spatial and temporal data drift.

MATERIALS AND METHODS : We obtained four datasets, identified by the location: 1-large academic hospital and 2-rural hospital, and time period: pre-COVID (Jan 1, 2019-Feb 1, 2020) and COVID-era (May 15, 2020-Feb 1, 2021). Our primary target was a binary outcome that is equal to 1 if the number of patients with acute respiratory illness that were ED boarding for more than four hours was above a prescribed historical percentile. We trained a random forest and used the area under the curve (AUC) to evaluate out-of-sample performance for two experiments: 1) we evaluated the impact of sudden temporal drift by training models using pre-COVID data and testing them during the COVID-era, 2) we evaluated the impact of spatial drift by testing models trained at Location 1 on data from Location 2, and vice versa.

RESULTS : The baseline AUC values for ED boarding ranged from 0.54 (pre-COVID at Location 2) to 0.81 (COVID-era at Location 1). Models trained with pre-COVID data performed similarly to COVID-era models (0.82 vs. 0.78 at Location 1). Models that were transferred from Location 2 to Location 1 performed worse than models trained at Location 1 (0.51 vs. 0.78).

DISCUSSION AND CONCLUSION : Our results demonstrate that ED boarding is a predictable metric for ED crowding, models were not significantly impacted by temporal data drift, and any attempts at implementation must consider spatial data drift.

Smith Ari J, Patterson Brian W, Pulia Michael S, Mayer John, Schwei Rebecca J, Nagarajan Radha, Liao Frank, Shah Manish N, Boutilier Justin J

2022-Oct-29

COVID-19, Emergency medicine, data drift, emergency department boarding, machine learning

Public Health Public Health

An exploration of challenges associated with machine learning for time series forecasting of COVID-19 community spread using wastewater-based epidemiological data.

In The Science of the total environment

Wastewater-based epidemiology (WBE) has gained increasing attention as a complementary tool to conventional surveillance methods with potential for significant resource and labour savings when used for public health monitoring. Using WBE datasets to train machine learning algorithms and develop predictive models may also facilitate early warnings for the spread of outbreaks. The challenges associated with implementing Random Forest (RF) for timeseries forecasting of COVID-19 was evaluated by running RF on WBE datasets across 108 sites in five regions: Scotland, Catalonia, Ohio, the Netherlands, and Switzerland. This method uses measurements of SARS-CoV-2 RNA fragment concentration in samples taken at the inlets of wastewater treatment plants, providing insight into the prevalence of infection in upstream wastewater catchment populations. RF's forecasting performance at each site was quantitatively evaluated by determining mean absolute percentage error (MAPE) values, which was used to highlight challenges affecting future implementations of RF for WBE forecasting efforts. Performance was generally poor using WBE datasets from Catalonia, Scotland, and Ohio with 'reasonable' or better forecasts constituting 0 %, 5 %, and 0 % of these regions' forecasts, respectively. RF's performance was much stronger with WBE data from the Netherlands and Switzerland, which provided 55 % and 45 % 'reasonable' or better forecasts respectively. Sampling frequency and training set size were identified as key factors contributing to accuracy, while inclusion of too many unnecessary variables (or e.g., flow data) was identified as a contributing factor to poor performance. The contribution of catchment population on forecast accuracy was more ambiguous. This study determined that the factors governing RF's forecast performance are complicated and interrelated, which presents challenges for further work in this space. A sufficiently accurate further iteration of the tool discussed within this study would provide significant but varying value for public health departments for monitoring future, or ongoing outbreaks, assisting the implementation of on-time health response measures.

Vaughan Liam, Zhang Muyang, Gu Haoran, Rose Joan, Naughton Colleen, Medema Gertjan, Allan Vajra, Roiko Anne, Blackall Linda, Zamyadi Arash

2022-Oct-25

COVID-19, Machine learning, Time series forecasting, Wastewater-based epidemiology

General General

Assessing data gathering of chatbot based symptom checkers - a clinical vignettes study.

In International journal of medical informatics ; h5-index 49.0

BACKGROUND : The burden on healthcare systems is mounting continuously owing to population growth and aging, overuse of medical services, and the recent COVID-19 pandemic. This overload is also causing reduced healthcare quality and outcomes. One solution gaining momentum is the integration of intelligent self-assessment tools, known as symptom-checkers, into healthcare-providers' systems. To the best of our knowledge, no study so far has investigated the data-gathering capabilities of these tools, which represent a crucial resource for simulating doctors' skills in medical-interviews.

OBJECTIVES : The goal of this study was to evaluate the data-gathering function of currently available chatbot symptom-checkers.

METHODS : We evaluated 8 symptom-checkers using 28 clinical vignettes from the repository of MSD-Manual case studies. The mean number of predefined pertinent findings for each case was 31.8 ± 6.8. The vignettes were entered into the platforms by 3 medical students who simulated the role of the patient. For each conversation, we obtained the number of pertinent findings retrieved and the number of questions asked. We then calculated the recall-rates (pertinent-findings retrieved out of all predefined pertinent-findings), and efficiency-rates (pertinent-findings retrieved out of the number of questions asked) of data-gathering, and compared them between the platforms.

RESULTS : The overall recall rate for all symptom-checkers was 0.32(2,280/7,112;95 %CI 0.31-0.33) for all pertinent findings, 0.37(1,110/2,992;95 %CI 0.35-0.39) for present findings, and 0.28(1140/4120;95 %CI 0.26-0.29) for absent findings. Among the symptom-checkers, Kahun platform had the highest recall rate with 0.51(450/889;95 %CI 0.47-0.54). Out of 4,877 questions asked overall, 2,280 findings were gathered, yielding an efficiency rate of 0.46(95 %CI 0.45-0.48) across all platforms. Kahun was the most efficient tool 0.74 (95 %CI 0.70-0.77) without a statistically significant difference from Your.MD 0.69(95 %CI 0.65-0.73).

CONCLUSION : The data-gathering performance of currently available symptom checkers is questionable. From among the tools available, Kahun demonstrated the best overall performance.

Ben-Shabat Niv, Sharvit Gal, Meimis Ben, Ben Joya Daniel, Sloma Ariel, Kiderman David, Shabat Aviv, Tsur Avishai M, Watad Abdulla, Amital Howard

2022-Oct-22

Artificial intelligence, Chatbots, Computer-assisted diagnosis, Data-gathering, Diagnosis, Medical interview, Symptom checker, Telemedicine, Triage

General General

Genome-wide detection of human variants that disrupt intronic branchpoints.

In Proceedings of the National Academy of Sciences of the United States of America

Pre-messenger RNA splicing is initiated with the recognition of a single-nucleotide intronic branchpoint (BP) within a BP motif by spliceosome elements. Forty-eight rare variants in 43 human genes have been reported to alter splicing and cause disease by disrupting BP. However, until now, no computational approach was available to efficiently detect such variants in massively parallel sequencing data. We established a comprehensive human genome-wide BP database by integrating existing BP data and generating new BP data from RNA sequencing of lariat debranching enzyme DBR1-mutated patients and from machine-learning predictions. We characterized multiple features of BP in major and minor introns and found that BP and BP-2 (two nucleotides upstream of BP) positions exhibit a lower rate of variation in human populations and higher evolutionary conservation than the intronic background, while being comparable to the exonic background. We developed BPHunter as a genome-wide computational approach to systematically and efficiently detect intronic variants that may disrupt BP recognition. BPHunter retrospectively identified 40 of the 48 known pathogenic BP variants, in which we summarized a strategy for prioritizing BP variant candidates. The remaining eight variants all create AG-dinucleotides between the BP and acceptor site, which is the likely reason for missplicing. We demonstrated the practical utility of BPHunter prospectively by using it to identify a novel germline heterozygous BP variant of STAT2 in a patient with critical COVID-19 pneumonia and a novel somatic intronic 59-nucleotide deletion of ITPKB in a lymphoma patient, both of which were validated experimentally. BPHunter is publicly available from https://hgidsoft.rockefeller.edu/BPHunter and https://github.com/casanova-lab/BPHunter.

Zhang Peng, Philippot Quentin, Ren Weicheng, Lei Wei-Te, Li Juan, Stenson Peter D, Palacín Pere Soler, Colobran Roger, Boisson Bertrand, Zhang Shen-Ying, Puel Anne, Pan-Hammarström Qiang, Zhang Qian, Cooper David N, Abel Laurent, Casanova Jean-Laurent

2022-Nov

branchpoint, disease genetics, intronic variant, software, splicing

General General

Artificial Intelligence and Deep Learning Assisted Rapid Diagnosis of COVID-19 from Chest Radiographical Images: A Survey.

In Contrast media & molecular imaging

Artificial Intelligence (AI) has been applied successfully in many real-life domains for solving complex problems. With the invention of Machine Learning (ML) paradigms, it becomes convenient for researchers to predict the outcome based on past data. Nowadays, ML is acting as the biggest weapon against the COVID-19 pandemic by detecting symptomatic cases at an early stage and warning people about its futuristic effects. It is observed that COVID-19 has blown out globally so much in a short period because of the shortage of testing facilities and delays in test reports. To address this challenge, AI can be effectively applied to produce fast as well as cost-effective solutions. Plenty of researchers come up with AI-based solutions for preliminary diagnosis using chest CT Images, respiratory sound analysis, voice analysis of symptomatic persons with asymptomatic ones, and so forth. Some AI-based applications claim good accuracy in predicting the chances of being COVID-19-positive. Within a short period, plenty of research work is published regarding the identification of COVID-19. This paper has carefully examined and presented a comprehensive survey of more than 110 papers that came from various reputed sources, that is, Springer, IEEE, Elsevier, MDPI, arXiv, and medRxiv. Most of the papers selected for this survey presented candid work to detect and classify COVID-19, using deep-learning-based models from chest X-Rays and CT scan images. We hope that this survey covers most of the work and provides insights to the research community in proposing efficient as well as accurate solutions for fighting the pandemic.

Sinwar Deepak, Dhaka Vijaypal Singh, Tesfaye Biniyam Alemu, Raghuwanshi Ghanshyam, Kumar Ashish, Maakar Sunil Kr, Agrawal Sanjay

2022

Radiology Radiology

Contrastive learning and subtyping of post-COVID-19 lung computed tomography images.

In Frontiers in physiology

Patients who recovered from the novel coronavirus disease 2019 (COVID-19) may experience a range of long-term symptoms. Since the lung is the most common site of the infection, pulmonary sequelae may present persistently in COVID-19 survivors. To better understand the symptoms associated with impaired lung function in patients with post-COVID-19, we aimed to build a deep learning model which conducts two tasks: to differentiate post-COVID-19 from healthy subjects and to identify post-COVID-19 subtypes, based on the latent representations of lung computed tomography (CT) scans. CT scans of 140 post-COVID-19 subjects and 105 healthy controls were analyzed. A novel contrastive learning model was developed by introducing a lung volume transform to learn latent features of disease phenotypes from CT scans at inspiration and expiration of the same subjects. The model achieved 90% accuracy for the differentiation of the post-COVID-19 subjects from the healthy controls. Two clusters (C1 and C2) with distinct characteristics were identified among the post-COVID-19 subjects. C1 exhibited increased air-trapping caused by small airways disease (4.10%, p = 0.008) and diffusing capacity for carbon monoxide %predicted (DLCO %predicted, 101.95%, p < 0.001), while C2 had decreased lung volume (4.40L, p < 0.001) and increased ground glass opacity (GGO%, 15.85%, p < 0.001). The contrastive learning model is able to capture the latent features of two post-COVID-19 subtypes characterized by air-trapping due to small airways disease and airway-associated interstitial fibrotic-like patterns, respectively. The discovery of post-COVID-19 subtypes suggests the need for different managements and treatments of long-term sequelae of patients with post-COVID-19.

Li Frank, Zhang Xuan, Comellas Alejandro P, Hoffman Eric A, Yang Tianbao, Lin Ching-Long

2022

PASC, cluster analysis, computed tomography, contrastive learning, long Covid, post-COVID-19, small airways disease

General General

A multi-task FP-GNN framework enables accurate prediction of selective PARP inhibitors.

In Frontiers in pharmacology

PARP (poly ADP-ribose polymerase) family is a crucial DNA repair enzyme that responds to DNA damage, regulates apoptosis, and maintains genome stability; therefore, PARP inhibitors represent a promising therapeutic strategy for the treatment of various human diseases including COVID-19. In this study, a multi-task FP-GNN (Fingerprint and Graph Neural Networks) deep learning framework was proposed to predict the inhibitory activity of molecules against four PARP isoforms (PARP-1, PARP-2, PARP-5A, and PARP-5B). Compared with baseline predictive models based on four conventional machine learning methods such as RF, SVM, XGBoost, and LR as well as six deep learning algorithms such as DNN, Attentive FP, MPNN, GAT, GCN, and D-MPNN, the evaluation results indicate that the multi-task FP-GNN method achieves the best performance with the highest average BA, F1, and AUC values of 0.753 ± 0.033, 0.910 ± 0.045, and 0.888 ± 0.016 for the test set. In addition, Y-scrambling testing successfully verified that the model was not results of chance correlation. More importantly, the interpretability of the multi-task FP-GNN model enabled the identification of key structural fragments associated with the inhibition of each PARP isoform. To facilitate the use of the multi-task FP-GNN model in the field, an online webserver called PARPi-Predict and its local version software were created to predict whether compounds bear potential inhibitory activity against PARPs, thereby contributing to design and discover better selective PARP inhibitors.

Ai Daiqiao, Wu Jingxing, Cai Hanxuan, Zhao Duancheng, Chen Yihao, Wei Jiajia, Xu Jianrong, Zhang Jiquan, Wang Ling

2022

PARP, deep learning, interpretability, multi-task FP-GNN, online webserver

General General

We are not ready yet: limitations of state-of-the-art disease named entity recognizers.

In Journal of biomedical semantics ; h5-index 23.0

BACKGROUND : Intense research has been done in the area of biomedical natural language processing. Since the breakthrough of transfer learning-based methods, BERT models are used in a variety of biomedical and clinical applications. For the available data sets, these models show excellent results - partly exceeding the inter-annotator agreements. However, biomedical named entity recognition applied on COVID-19 preprints shows a performance drop compared to the results on test data. The question arises how well trained models are able to predict on completely new data, i.e. to generalize.

RESULTS : Based on the example of disease named entity recognition, we investigate the robustness of different machine learning-based methods - thereof transfer learning - and show that current state-of-the-art methods work well for a given training and the corresponding test set but experience a significant lack of generalization when applying to new data.

CONCLUSIONS : We argue that there is a need for larger annotated data sets for training and testing. Therefore, we foresee the curation of further data sets and, moreover, the investigation of continual learning processes for machine learning-based models.

Kühnel Lisa, Fluck Juliane

2022-Oct-27

BERT, Manual Curation, Text mining, bioNLP

General General

Vitamin D deficiency and SARS-CoV-2 infection: Big-data analysis from March 2020 to March 2021. D-COVID study

bioRxiv Preprint

Methods: Using big-data analytics and artificial intelligence through the SAVANA Manager clinical platform, we analysed clinical data from patients with COVID-19 atended in a terciary university hospital from March 2020 to March 2021. Results: Of the 143.157 analysed patients, 36.261 subjects had COVID-19 infection (25.33%); during this period; of these 2588 had vitamin D deficiency (7.14%). Among subjects with COVID-19 and vitamin D deficiency, there was a higher proportion of women OR 1.45 [95% CI 1.33-1.57], adults older than 80 years OR 2.63 [95%CI 2.38-2.91], people living in nursing homes OR 2.88 [95%CI 2.95-3.45] and walking dependence OR 3.45 [95%CI 2.85-4.26]. Regarding clinical course, a higher number of subjects with COVID-19 and vitamin D deficiency required hospitalitation OR 2.41 [95%CI 2.22-2-61], intensive unit care (ICU) OR 2.22 [95% CI 1.64-3.02], had a longer mean hospital stay 3.94 (2.29) p=0.02 and higher mortality OR 1.82 [95%CI 1.66-2.01].) Conclusion: Low serum 25 (OH) Vitamin-D level was significantly associated with a worse clinical evolution and prognosis of COVID-19 infection. We found a higher proportion of institutionalised and dependent people over 80 years of age among patients with COVID-19 and vitamin D deficiency.

Anguita, N.

2022-10-28

Public Health Public Health

Machine learning predicts the short-term requirement for invasive ventilation among Australian critically ill COVID-19 patients.

In PloS one ; h5-index 176.0

OBJECTIVE(S) : To use machine learning (ML) to predict short-term requirements for invasive ventilation in patients with COVID-19 admitted to Australian intensive care units (ICUs).

DESIGN : A machine learning study within a national ICU COVID-19 registry in Australia.

PARTICIPANTS : Adult patients who were spontaneously breathing and admitted to participating ICUs with laboratory-confirmed COVID-19 from 20 February 2020 to 7 March 2021. Patients intubated on day one of their ICU admission were excluded.

MAIN OUTCOME MEASURES : Six machine learning models predicted the requirement for invasive ventilation by day three of ICU admission from variables recorded on the first calendar day of ICU admission; (1) random forest classifier (RF), (2) decision tree classifier (DT), (3) logistic regression (LR), (4) K neighbours classifier (KNN), (5) support vector machine (SVM), and (6) gradient boosted machine (GBM). Cross-validation was used to assess the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of machine learning models.

RESULTS : 300 ICU admissions collected from 53 ICUs across Australia were included. The median [IQR] age of patients was 59 [50-69] years, 109 (36%) were female and 60 (20%) required invasive ventilation on day two or three. Random forest and Gradient boosted machine were the best performing algorithms, achieving mean (SD) AUCs of 0.69 (0.06) and 0.68 (0.07), and mean sensitivities of 77 (19%) and 81 (17%), respectively.

CONCLUSION : Machine learning can be used to predict subsequent ventilation in patients with COVID-19 who were spontaneously breathing and admitted to Australian ICUs.

Karri Roshan, Chen Yi-Ping Phoebe, Burrell Aidan J C, Penny-Dimri Jahan C, Broadley Tessa, Trapani Tony, Deane Adam M, Udy Andrew A, Plummer Mark P

2022

General General

Pseudo-Label Guided Image Synthesis for Semi-Supervised COVID-19 Pneumonia Infection Segmentation.

In IEEE transactions on medical imaging ; h5-index 74.0

Coronavirus disease 2019 (COVID-19) has become a severe global pandemic. Accurate pneumonia infection segmentation is important for assisting doctors in diagnosing COVID-19. Deep learning-based methods can be developed for automatic segmentation, but the lack of large-scale well-annotated COVID-19 training datasets may hinder their performance. Semi-supervised segmentation is a promising solution which explores large amounts of unlabelled data, while most existing methods focus on pseudo-label refinement. In this paper, we propose a new perspective on semi-supervised learning for COVID-19 pneumonia infection segmentation, namely pseudo-label guided image synthesis. The main idea is to keep the pseudo-labels and synthesize new images to match them. The synthetic image has the same COVID-19 infected regions as indicated in the pseudo-label, and the reference style extracted from the style code pool is added to make it more realistic. We introduce two representative methods by incorporating the synthetic images into model training, including single-stage Synthesis-Assisted Cross Pseudo Supervision (SA-CPS) and multi-stage Synthesis-Assisted Self-Training (SA-ST), which can work individually as well as cooperatively. Synthesis-assisted methods expand the training data with high-quality synthetic data, thus improving the segmentation performance. Extensive experiments on two COVID-19 CT datasets for segmenting the infections demonstrate our method is superior to existing schemes for semi-supervised segmentation, and achieves the state-of-the-art performance on both datasets. Code is available at: https://github.com/FeiLyu/SASSL.

Lyu Fei, Ye Mang, Carlsen Jonathan Frederik, Erleben Kenny, Darkner Sune, Yuen Pong C

2022-Oct-26

Public Health Public Health

Valuation of Costs in Health Economics During Financial and Economic Crises: A Case Study from Lebanon.

In Applied health economics and health policy

In 2019, we embarked on a study on the economic burden of multiple sclerosis (MS) in Lebanon, in collaboration with a premier Lebanese MS center. This coincided with a triple disaster in Lebanon, comprising the drastic economic and financial crisis, the COVID-19 pandemic, and the consequences of the explosion of Beirut's port. Specifically, the economic and financial turmoil made the valuation of costs challenging. Researchers could face similar challenges, particularly in low- and middle-income countries (LMICs) where economic crises and recessions are recurrent phenomena. This paper aims to discuss steps taken to overcome the fluctuation of the prices of resources to get a valid valuation of societal costs during times of a financial and economic crisis. In the absence of local costing data and guidelines for conducting cost-of-illness (COI) studies, this paper provides empirical recommendations on the valuation of costs that are particularly relevant in LMICs. We recommend (1) clear reporting and justification of the country-specific context, year of costing, assumptions, data sources, and valuation methods, as well as the indicators used to adjust cost for inflation during different periods of fluctuation of prices; (2) collecting prices of each resource from multiple and various sources; (3) conducting a sensitivity analysis; and (4) reporting costs in local currency and Purchasing Power Parity dollars (PPP$). Precision and transparency in reporting prices of resources and their sources are markers of the reliability of the COI studies.

Dahham Jalal, Kremer Ingrid, Hiligsmann Mickaël, Hamdan Kamal, Nassereddine Abdallah, Evers Silvia M A A, Rizk Rana

2022-Oct-26

General General

[The role of artificial intelligence in assessing the progression of fibrosing lung diseases].

In Terapevticheskii arkhiv

INTRODUCTION : The widespread use of artificial intelligence (AI) programs during the COVID-19 pandemic to assess the exact volume of lung tissue damage has allowed them to train a large number of radiologists. The simplicity of the program for determining the volume of the affected lung tissue in acute interstitial pneumonia, which has density indicators in the range from -200 HU to -730 HU, which includes the density indicators of "ground glass" and reticulation (the main radiation patterns in COVID-19) allows you to accurately determine the degree of prevalence process. The characteristics of chronic interstitial pneumonia, which are progressive in nature, fit into the same density framework. Аim. To аssess AI's ability to assess the progression of fibrosing lung disease using lung volume counting programs used for COVID-19 and chronic obstructive pulmonary disease.

RESULTS : Retrospective analysis of computed tomography data during follow-up of 75 patients with progressive fibrosing lung disease made it possible to assess the prevalence and growth of interstitial lesions.

CONCLUSION : Using the experience of using AI programs to assess acute interstitial pneumonia in COVID-19 can be applied to chronic interstitial pneumonia.

Speranskaia A A

2022-Mar-15

artificial intelligence, computer tomography, progressive fibrosing interstitial lung diseases

General General

Comprehensive Survey of Machine Learning Systems for COVID-19 Detection.

In Journal of imaging

The last two years are considered the most crucial and critical period of the COVID-19 pandemic affecting most life aspects worldwide. This virus spreads quickly within a short period, increasing the fatality rate associated with the virus. From a clinical perspective, several diagnosis methods are carried out for early detection to avoid virus propagation. However, the capabilities of these methods are limited and have various associated challenges. Consequently, many studies have been performed for COVID-19 automated detection without involving manual intervention and allowing an accurate and fast decision. As is the case with other diseases and medical issues, Artificial Intelligence (AI) provides the medical community with potential technical solutions that help doctors and radiologists diagnose based on chest images. In this paper, a comprehensive review of the mentioned AI-based detection solution proposals is conducted. More than 200 papers are reviewed and analyzed, and 145 articles have been extensively examined to specify the proposed AI mechanisms with chest medical images. A comprehensive examination of the associated advantages and shortcomings is illustrated and summarized. Several findings are concluded as a result of a deep analysis of all the previous works using machine learning for COVID-19 detection, segmentation, and classification.

Alsaaidah Bayan, Al-Hadidi Moh’d Rasoul, Al-Nsour Heba, Masadeh Raja, AlZubi Nael

2022-Sep-30

COVID-19, CT images, augmentation, deep learning, diagnosis, machine learning, pneumonia

General General

Deep Transfer Learning for COVID-19 Detection and Lesion Recognition Using Chest CT Images.

In Computational and mathematical methods in medicine

Starting from December 2019, the global pandemic of coronavirus disease 2019 (COVID-19) is continuously expanding and has caused several millions of deaths worldwide. Fast and accurate diagnostic methods for COVID-19 detection play a vital role in containing the plague. Chest computed tomography (CT) is one of the most commonly used diagnosis methods. However, a complete CT-scan has hundreds of slices, and it is time-consuming for radiologists to check each slice to diagnose COVID-19. This study introduces a novel method for fast and automated COVID-19 diagnosis using the chest CT scans. The proposed models are based on the state-of-the-art deep convolutional neural network (CNN) architecture, and a 2D global max pooling (globalMaxPool2D) layer is used to improve the performance. We compare the proposed models to the existing state-of-the-art deep learning models such as CNN based models and vision transformer (ViT) models. Based off of metric such as area under curve (AUC), sensitivity, specificity, accuracy, and false discovery rate (FDR), experimental results show that the proposed models outperform the previous methods, and the best model achieves an area under curve of 0.9744 and accuracy 94.12% on our test datasets. It is also shown that the accuracy is improved by around 1% by using the 2D global max pooling layer. Moreover, a heatmap method to highlight the lesion area on COVID-19 chest CT images is introduced in the paper. This heatmap method is helpful for a radiologist to identify the abnormal pattern of COVID-19 on chest CT images. In addition, we also developed a freely accessible online simulation software for automated COVID-19 detection using CT images. The proposed deep learning models and software tool can be used by radiologist to diagnose COVID-19 more accurately and efficiently.

Zhang Sai, Yuan Guo-Chang

2022

Public Health Public Health

A novel infrasound and audible machine-learning approach to the diagnosis of COVID-19.

In ERJ open research

Background : The coronavirus disease 2019 (COVID-19) outbreak has rapidly spread around the world, causing a global public health and economic crisis. A critical limitation in detecting COVID-19-related pneumonia is that it is often manifested as a "silent pneumonia", i.e. pulmonary auscultation that sounds "normal" using a standard stethoscope. Chest computed tomography is the gold standard for detecting COVID-19 pneumonia; however, radiation exposure, availability and cost preclude its utilisation as a screening tool for COVID-19 pneumonia. In this study we hypothesised that COVID-19 pneumonia, "silent" to the human ear using a standard stethoscope, is detectable using a full-spectrum auscultation device that contains a machine-learning analysis.

Methods : Lung sound signals were acquired, using a novel full-spectrum (3-2000 Hz) stethoscope, from 164 COVID-19 pneumonia patients, 61 non-COVID-19 pneumonia patients and 141 healthy subjects. A machine-learning classifier was constructed and the data were classified into three groups: 1) normal lung sounds, 2) COVID-19 pneumonia and 3) non-COVID-19 pneumonia.

Results : Standard auscultation found that 72% of the non-COVID-19 pneumonia patients had abnormal lung sounds compared with only 25% of the COVID-19 pneumonia patients. The classifier's sensitivity and specificity for the detection of COVID-19 pneumonia were 97% and 93%, respectively, when analysing the sound and infrasound data, and they were reduced to 93% and 80%, respectively, without the infrasound data (p<0.01 difference in receiver operating characteristic curves with and without infrasound).

Conclusions : This study reveals that useful clinical information exists in the infrasound spectrum of COVID-19-related pneumonia and machine-learning analysis applied to the full spectrum of lung sounds is useful in its detection.

Dori Guy, Bachner-Hinenzon Noa, Kasim Nour, Zaidani Haitem, Perl Sivan Haia, Maayan Shlomo, Shneifi Amin, Kian Yousef, Tiosano Tuvia, Adler Doron, Adir Yochai

2022-Oct

General General

BND-VGG-19: A deep learning algorithm for COVID-19 identification utilizing X-ray images.

In Knowledge-based systems

During the past two years, a highly infectious virus known as COVID-19 has been damaging and harming the health of people all over the world. Simultaneously, the number of patients is rising in various countries, with many new cases appearing daily, posing a significant challenge to hospital medical staff. It is necessary to improve the efficiency of virus detection. To this end, we combine modern technology and visual assistance to detect COVID-19. Based on the above facts, for accurate and rapid identification of infected persons, the BND-VGG-19 method was proposed. This method is based on VGG-19 and further incorporates batch normalization and dropout layers between the layers to improve network accuracy. Then, the COVID-19 dataset including viral pneumonia, COVID-19, and normal X-ray images, are used to diagnose lung abnormalities and test the performance of the proposed algorithm. The experimental results show the superiority of BND-VGG-19 with a 95.48% accuracy rate compared with existing COVID-19 diagnostic methods.

Cao Zili, Huang Junjian, He Xing, Zong Zhaowen

2022-Oct-21

COVID-19, Classification, Diagnosis, VGG-19, X-ray image

Public Health Public Health

COVID-19 Vaccination Rates of People Who Use Drugs - Chengdu City, Sichuan Province, China, November 2021 - February 2022.

In China CDC weekly

What is already known about this topic? : Few studies have reported that people who use drugs (PWUDs) have much lower coronavirus disease 2019 (COVID-19) vaccination rates than the general population, especially with no relative information reported in China specifically.

What is added by this report? : This study seminally uncovers that the vaccination rate among PWUDs was about 79.34% in one district of Chengdu City, Sichuan Province, China. Assuming that unvaccinated PWUDs with disease records were really not eligible for vaccination, the vaccination rate goes up to 87.25% among the studied PWUDs. The study implies that PWUDs were not left behind in the vaccination drive against COVID-19 in China.

What are the implications for public health practice? : In pandemics like COVID-19, government leadership and the overall planning and distribution of public health products are critical in achieving national health equity. However, in order to do this as well as avoid discrimination or exclusion among specific portions of the general population, it's necessary to understand the vaccination rates and behaviors of at-risk groups such as PWUD's.

Du Erri, Jiang Pengyu, Zhang Chaowei, Zhang Shan, Yan Xiangyu, Li Yongjie, Jia Zhongwei

2022-Sep-16

COVID-19, people who use drugs, vaccination

General General

Can Artificial Intelligence Detect Monkeypox from Digital Skin Images?

bioRxiv Preprint

An outbreak of Monkeypox has been reported in 75 countries so far, and it is spreading at a fast pace around the world. The clinical attributes of Monkeypox resemble those of Smallpox, while skin lesions and rashes of Monkeypox often resemble those of other poxes, for example, Chickenpox and Cowpox. These similarities make Monkeypox detection challenging for healthcare professionals by examining the visual appearance of lesions and rashes. Additionally, there is a knowledge gap among healthcare professionals due to the rarity of Monkeypox before the current outbreak. Motivated by the success of artificial intelligence (AI) in COVID-19 detection, the scientific community has shown an increasing interest in using AI in Monkeypox detection from digital skin images. However, the lack of Monkeypox skin image data has been the bottleneck of using AI in Monkeypox detection. Therefore, in this paper, we used a web-scrapping-based Monkeypox, Chickenpox, Smallpox, Cowpox, Measles, and healthy skin image dataset to study the feasibility of using state-of-the-art AI deep models on skin images for Monkeypox detection. Our study found that deep AI models have great potential in the detection of Monkeypox from digital skin images (precision of 85%). However, achieving a more robust detection power requires larger training samples to train those deep models.

Islam, T.; Hussain, M.; Chowdhury, F. U. H.; Islam, B. M. R.

2022-10-27

Internal Medicine Internal Medicine

Natural Language Processing CAM Algorithm Improves Delirium Detection Compared With Conventional Methods.

In American journal of medical quality : the official journal of the American College of Medical Quality

Delirium is known to be underdiagnosed and underdocumented. Delirium detection in retrospective studies occurs mostly by clinician diagnosis or nursing documentation. This study aims to assess the effectiveness of natural language processing-confusion assessment method (NLP-CAM) algorithm when compared to conventional modalities of delirium detection. A multicenter retrospective study analyzed 4351 COVID-19 hospitalized patient records to identify delirium occurrence utilizing three different delirium detection modalities namely clinician diagnosis, nursing documentation, and the NLP-CAM algorithm. Delirium detection by any of the 3 methods is considered positive for delirium occurrence as a comparison. NLP-CAM captured 80% of overall delirium, followed by clinician diagnosis at 55%, and nursing flowsheet documentation at 43%. Increase in age, Charlson comorbidity score, and length of hospitalization had increased delirium detection odds regardless of the detection method. Artificial intelligence-based NLP-CAM algorithm, compared to conventional methods, improved delirium detection from electronic health records and holds promise in delirium diagnostics.

Pagali Sandeep R, Kumar Rakesh, Fu Sunyang, Sohn Sunghwan, Yousufuddin Mohammed

2022-Oct-26

Public Health Public Health

Deep learning modelling of public's sentiments towards temporal evolution of COVID-19 transmission.

In Applied soft computing

Public sentiments towards global pandemics are important for public health assessment and disease control. This study develops a modularized deep learning framework to quantify public sentiments towards COVID-19, followed by leveraging the predicted sentiments to model and forecast the daily growth rate of confirmed COVID-19 cases globally, via a proposed G parameter. In the proposed framework, public sentiments are first modeled via a valence dimensional indicator, instead of discrete schemas, and are classified into 4 primary emotional categories: (a) neutral; (b) negative; (c) positive; (d) ambivalent, by using multiple word embedding models and classifiers for text sentiments analyses and classification. The trained model is subsequently applied to analyze large volumes (millions in quantity) of daily Tweets pertaining to COVID-19, ranging from 22 Jan 2020 to 10 May 2020. The results demonstrate that the global community gradually evokes both positive and negative sentiments towards COVID-19 over time compared to the dominant neural emotion at its inception. The predicted time-series sentiments are then leveraged to train a deep neural network (DNN) to model and forecast the G parameter by achieving the lowest possible mean absolute percentage error (MAPE) score of around 17.0% during the model's testing step with the optimal model configuration.

Wang Ying, Chew Alvin Wei Ze, Zhang Limao

2022-Oct-20

COVID-19 transmission, Deep learning, Global sentiment evolution, Natural language processing, Text sentiment classification, Twitter data

Public Health Public Health

COVICT: an IoT based architecture for COVID-19 detection and contact tracing.

In Journal of ambient intelligence and humanized computing

The world we live in has been taken quite surprisingly by the outbreak of a novel virus namely SARS-CoV-2. COVID-19 i.e. the disease associated with the virus, has not only shaken the world economy due to enforced lockdown but has also saturated the public health care systems of even most advanced countries due to its exponential spread. The fight against COVID-19 pandemic will continue until majority of world's population get vaccinated or herd immunity is achieved. Many researchers have exploited the Artificial intelligence (AI) knacks based IoT architecture for early detection and monitoring of potential COVID-19 cases to control the transmission of the virus. However, the main cause of the spread is that people infected with COVID-19 do not show any symptoms and are asymptomatic but can still transmit virus to the masses. Researcher have introduced contact tracing applications to automatically detect contacts that can be infected by the index case. However, these fully automated contact tracing apps have not been accepted due to issues like privacy and cross-app compatibility. In the current study, an IoT based COVID-19 detection and monitoring system with semi-automated and improved contact tracing capability namely COVICT has been presented with application of real-time data of symptoms collected from individuals and contact tracing. The deployment of COVICT, the prediction of infected persons can be made more effective and contaminated areas can be identified to mitigate the further propagation of the virus by imposing Smart Lockdown. The proposed IoT based architecture can be quite helpful for regulatory authorities for policy making to fight COVID-19.

Wahid Mirza Anas, Bukhari Syed Hashim Raza, Daud Ahmad, Awan Saeed Ehsan, Raja Muhammad Asif Zahoor

2022-Oct-20

COVID-19, Contact tracing, D2D communication, Early identification, Internet of things, Pandemic, Smart lockdown

General General

A machine learning study of COVID-19 serology and molecular tests and predictions.

In Smart health (Amsterdam, Netherlands)

Serology and molecular tests are the two most commonly used methods for rapid COVID-19 infection testing. The two types of tests have different mechanisms to detect infection, by measuring the presence of viral SARS-CoV-2 RNA (molecular test) or detecting the presence of antibodies triggered by the SARS-CoV-2 virus (serology test). A handful of studies have shown that symptoms, combined with demographic and/or diagnosis features, can be helpful for the prediction of COVID-19 test outcomes. However, due to nature of the test, serology and molecular tests vary significantly. There is no existing study on the correlation between serology and molecular tests, and what type of symptoms are the key factors indicating the COVID-19 positive tests. In this study, we propose a machine learning based approach to study serology and molecular tests, and use features to predict test outcomes. A total of 2,467 donors, each tested using one or multiple types of COVID-19 tests, are collected as our testbed. By cross checking test types and results, we study correlation between serology and molecular tests. For test outcome prediction, we label 2,467 donors as positive or negative, by using their serology or molecular test results, and create symptom features to represent each donor for learning. Because COVID-19 produces a wide range of symptoms and the data collection process is essentially error prone, we group similar symptoms into bins. This decreases the feature space and sparsity. Using binned symptoms, combined with demographic features, we train five classification algorithms to predict COVID-19 test results. Experiments show that XGBoost achieves the best performance with 76.85% accuracy and 81.4% AUC scores, demonstrating that symptoms are indeed helpful for predicting COVID-19 test outcomes. Our study investigates the relationship between serology and molecular tests, identifies meaningful symptom features associated with COVID-19 infection, and also provides a way for rapid screening and cost effective detection of COVID-19 infection.

Elkin Magdalyn E, Zhu Xingquan

2022-Oct-20

68T05, 68T50, 92C50, 92C55, 92C60, COVID-19, Classification, Machine Learning, Molecular test, Serology test, Symptoms

Public Health Public Health

Diagnostic performance of corona virus disease 2019 chest computer tomography image recognition based on deep learning: Systematic review and meta-analysis.

In Medicine

BACKGROUND : To analyze the diagnosis performance of deep learning model used in corona virus disease 2019 (COVID-19) computer tomography(CT) chest scans. The included sample contains healthy people, confirmed COVID-19 patients and unconfirmed suspected patients with corresponding symptoms.

METHODS : PubMed, Web of Science, Wiley, China National Knowledge Infrastructure, WAN FANG DATA, and Cochrane Library were searched for articles. Three researchers independently screened the literature, extracted the data. Any differences will be resolved by consulting the third author to ensure that a highly reliable and useful research paper is produced. Data were extracted from the final articles, including: authors, country of study, study type, sample size, participant demographics, type and name of AI software, results (accuracy, sensitivity, specificity, ROC, and predictive values), other outcome(s) if applicable.

RESULTS : Among the 3891 searched results, 32 articles describing 51,392 confirmed patients and 7686 non-infected individuals met the inclusion criteria. The pooled sensitivity, the pooled specificity, positive likelihood ratio, negative likelihood ratio and the pooled diagnostic odds ratio (OR) is 0.87(95%CI [confidence interval]: 0.85, 0.89), 0.85(95%CI: 0.82, 0.87), 6.7(95%CI: 5.7, 7.8), 0.14(95%CI: 0.12, 0.16), and 49(95%CI: 38, 65). Further, the AUROC (area under the receiver operating characteristic curve) is 0.94(95%CI: 0.91, 0.96). Secondary outcomes are specific sensitivity and specificity within subgroups defined by different models. Resnet has the best diagnostic performance, which has the highest sensitivity (0.91[95%CI: 0.87, 0.94]), specificity (0.90[95%CI: 0.86, 0.93]) and AUROC (0.96[95%CI: 0.94, 0.97]), according to the AUROC, we can get the rank Resnet > Densenet > VGG > Mobilenet > Inception > Effficient > Alexnet.

CONCLUSIONS : Our study findings show that deep learning models have immense potential in accurately stratifying COVID-19 patients and in correctly differentiating them from patients with other types of pneumonia and normal patients. Implementation of deep learning-based tools can assist radiologists in correctly and quickly detecting COVID-19 and, consequently, in combating the COVID-19 pandemic.

Wang Qiaolan, Ma Jingxuan, Zhang Luoning, Xie Linshen

2022-Oct-21

General General

Automated diagnosis of COVID-19 using radiological modalities and Artificial Intelligence functionalities: A retrospective study based on chest HRCT database.

In Biomedical signal processing and control

Background and Objective : : The spread of coronavirus has been challenging for the healthcare system's proper management and diagnosis during the rapid spread and control of the infection. Real-time reverse transcription-polymerase chain reaction (RT-PCR), though considered the standard testing measure, has low sensitivity and is time-consuming, which restricts the fast screening of individuals. Therefore, computer tomography (CT) is used to complement the traditional approaches and provide fast and effective screening over other diagnostic methods. This work aims to appraise the importance of chest CT findings of COVID-19 and post-COVID in the diagnosis and prognosis of infected patients and to explore the ways and means to integrate CT findings for the development of advanced Artificial Intelligence (AI) tool-based predictive diagnostic techniques.

Methods : : The retrospective study includes a 188 patient database with COVID-19 infection confirmed by RT-PCR testing, including post-COVID patients. Patients underwent chest high-resolution computer tomography (HRCT), where the images were evaluated for common COVID-19 findings and involvement of the lung and its lobes based on the coverage region. The radiological modalities analyzed in this study may help the researchers in generating a predictive model based on AI tools for further classification with a high degree of reliability.

Results : : Mild to moderate ground glass opacities (GGO) with or without consolidation, crazy paving patterns, and halo signs were common COVID-19 related findings. A CT score is assigned to every patient based on the severity of lung lobe involvement.

Conclusion : : Typical multifocal, bilateral, and peripheral distributions of GGO are the main characteristics related to COVID-19 pneumonia. Chest HRCT can be considered a standard method for timely and efficient assessment of disease progression and management severity. With its fusion with AI tools, chest HRCT can be used as a one-stop platform for radiological investigation and automated diagnosis system.

Bhattacharjya Upasana, Sarma Kandarpa Kumar, Medhi Jyoti Prakash, Choudhury Binoy Kumar, Barman Geetanjali

2022-Oct-18

COVID-19, Consolidation, Crazy –paving, Deep learning, Ground glass opacities, Halo Sign, Machine Learning

Surgery Surgery

Unsupervised clustering reveals phenotypes of AKI in ICU COVID-19 patients.

In Frontiers in medicine

Background : Acute Kidney Injury (AKI) is a very frequent condition, occurring in about one in three patients admitted to an intensive care unit (ICU). AKI is a syndrome defined as a sudden decrease in glomerular filtration rate. However, this unified definition does not reflect the various mechanisms involved in AKI pathophysiology, each with its own characteristics and sensitivity to therapy. In this study, we aimed at developing an innovative machine learning based method able to subphenotype AKI according to its pattern of risk factors.

Methods : We adopted a three-step pipeline of analyses. First, we looked for factors associated with AKI using a generalized additive model. Second, we calculated the importance of each identified AKI related factor in the estimated AKI risk to find the main risk factor for AKI, at the single patient level. Lastly, we clusterized AKI patients according to their profile of risk factors and compared the clinical characteristics and outcome of every cluster. We applied this method to a cohort of severe COVID-19 patients hospitalized in the ICU of the Geneva University Hospitals.

Results : Among the 248 patients analyzed, we found 7 factors associated with AKI development. Using the individual expression of these factors, we identified three groups of AKI patients, based on the use of Lopinavir/Ritonavir, baseline eGFR, use of dexamethasone and AKI severity. The three clusters expressed distinct characteristics in terms of AKI severity and recovery, metabolic patterns and hospital mortality.

Conclusion : We propose here a new method to phenotype AKI patients according to their most important individual risk factors for AKI development. When applied to an ICU cohort of COVID-19 patients, we were able to differentiate three groups of patients. Each expressed specific AKI characteristics and outcomes, which probably reflect a distinct pathophysiology.

Legouis David, Criton Gilles, Assouline Benjamin, Le Terrier Christophe, Sgardello Sebastian, Pugin Jérôme, Marchi Elisa, Sangla Frédéric

2022

AKI, COVID-19, clustering, critical care, machine learning

General General

Real-Time Analysis of Predictors of COVID-19 Infection Spread in Countries in the European Union Through a New Tool.

In International journal of public health

Objectives: Real-time data analysis during a pandemic is crucial. This paper aims to introduce a novel interactive tool called Covid-Predictor-Tracker using several sources of COVID-19 data, which allows examining developments over time and across countries. Exemplified here by investigating relative effects of vaccination to non-pharmaceutical interventions on COVID-19 spread. Methods: We combine >100 indicators from the Global COVID-19 Trends and Impact Survey, Johns Hopkins University, Our World in Data, European Centre for Disease Prevention and Control, National Centers for Environmental Information, and Eurostat using random forests, hierarchical clustering, and rank correlation to predict COVID-19 cases. Results: Between 2/2020 and 1/2022, we found among the non-pharmaceutical interventions "mask usage" to have strong effects after the percentage of people vaccinated at least once, followed by country-specific measures such as lock-downs. Countries with similar characteristics share ranks of infection predictors. Gender and age distribution, healthcare expenditures and cultural participation interact with restriction measures. Conclusion: Including time-aware machine learning models in COVID-19 infection dashboards allows to disentangle and rank predictors of COVID-19 cases per country to support policy evaluation. Our open-source tool can be updated daily with continuous data streams, and expanded as the pandemic evolves.

Balogh Aniko, Harman Anna, Kreuter Frauke

2022

COVID-19 non-pharmaceutical interventions, COVID-19 prediction, COVID-19 virus variants, comparative analyses, interactive visualization, machine learning, social epidemiology, time series cross-validation

General General

Search queries related to COVID-19 based on keyword extraction.

In Procedia computer science

Background : : Pandemic COVID-19 caused an infodemic - massive spread of true and fake information about novel coronavirus. This study aims to present the possibility of using Keyword Extraction as a tool to obtain the most trending search queries related to COVID-19 and analyze the possibility of including their search volume in models for the prediction of fake news.

Methods : : The study used Python implementation of the machine learning-based technique KeyBERT to extract keywords from true and fake news. These keywords were used in the next step to obtain related search queries with Google Trends API.

Results : : Non-parametric Spearman Rank Order Correlation was identified as a statistically positive correlation (p < 0.001) between the occurrence of false news and top query / rising query metrics provided by Google Trends of queries related to extracted keywords pandemic, HIV, lockdown, plague, Michigan, and protest, which proves that search volume can identify fake news.

Conclusions : : Experiments done in this research proved that Keyword Extraction from false news is useful for obtaining related search queries and the top query and rising query metrics can be used to increase the accuracy of fake news prediction models.

Kelebercová Lívia, Munk Michal

2022

Fake News Detection, Google Trends, Keyword Extraction, Natural Language Processing

General General

The Deep Learning-Based Framework for Automated Predicting COVID-19 Severity Score.

In Procedia computer science

With the COVID-19 pandemic sweeping the globe, an increasing number of people are working on pandemic research, but there is less effort on predicting its severity. Diagnostic chest imaging is thought to be a quick and reliable way to identify the severity of COVID-19. We describe a deep learning method to automatically predict the severity score of patients by analyzing chest X-rays, with the goal of collaborating with doctors to create corresponding treatment measures for patients and can also be used to track disease change. Our model consists of a feature extraction phase and an outcome prediction phase. The feature extraction phase uses a DenseNet backbone network to extract 18 features related to lung diseases from CXRs; the outcome prediction phase, which employs the MLP regression model, selects several important features for prediction from the features extracted in the previous phase and demonstrates the effectiveness of our model by comparing it with several commonly used regression models. On a dataset of 2373 CXRs, our model predicts the geographic extent score with 1.02 MAE and the lung opacity score with 0.85 MAE.

Zheng Yongchang, Dong Hongwei

2022

COVID-19, CXRs, deep learning method, estimate severity, feature extraction

General General

Machine Learning Models for Predicting Short-Long Length of Stay of COVID-19 Patients.

In Procedia computer science

During 2020 and 2021, managing limited healthcare resources and hospital beds has been a fundamental aspect of the fight against the COVID-19 pandemic. Predicting in advance the length of stay, and in particular identifying whether a patient is going to stay in the hospital longer or less than a week, can provide important support in handling resources allocation. However, there have been significant changes in terms of containment measures, virus diffusion, new treatments, vaccines, and new variants of SARS-CoV-2 during the last period. These changes pose several conceptual drift issues that can limit the usefulness of machine learning in this context. In this work, we present a machine learning system trained and tested using data from more than 6000 hospitalised patients in northern Italy, distributed over almost two years of pandemic. We show how machine learning can be effective even by analysing data over this long period of time, also exploiting a model that predicts the patient's outcome in terms of discharge or death. Furthermore, learning from data that also consider deceased patients is a common issue in predicting the length of stay because they have severe conditions similar to patients with a long stay period, but may actually have a very short duration of hospitalisation. For this purpose, we present a method for handling data from alive and deceased patients, exploiting more patient records, increasing the robustness of the model and its performance in this task. Finally, we investigate the features that are most relevant to the prediction of the simplified length of stay.

Olivato Matteo, Rossetti Nicholas, Gerevini Alfonso E, Chiari Mattia, Putelli Luca, Serina Ivan

2022

General General

The Influence of Environmental Factors on the Spread of COVID-19 in Italy.

In Procedia computer science

The aim of this work is to investigate possible relationships between air quality and the spread of the pandemic. We evaluate the performance of machine learning techniques in predicting new cases. Specifically, we describe a cross-correlation analysis on daily COVID-19 cases and environmental factors, such as temperature, relative humidity, and atmospheric pollutants. Our analysis confirms a significant association of some environmental parameters with the spread of the virus. This suggests that machine learning models trained using environmental parameters might provide accurate predictions about the number of infected cases. Our empirical evaluation shows that temperature and ozone are negatively correlated with confirmed cases (therefore, the higher the values of these parameters, the lower the number of infected cases), whereas atmospheric particulate matter and nitrogen dioxide are positively correlated. We developed and compared three different predictive models to test whether these technologies can be useful to estimate the evolution of the pandemic.

Loreggia Andrea, Passarelli Anna, Pini Maria Silvia

2022

Air Quality Effects, COVID-19 Pandemic, Correlation Analysis, Machine Learning

General General

An explainable COVID-19 detection system based on human sounds.

In Smart health (Amsterdam, Netherlands)

Acoustic signals generated by the human body have often been used as biomarkers to diagnose and monitor diseases. As the pathogenesis of COVID-19 indicates impairments in the respiratory system, digital acoustic biomarkers of COVID-19 are under investigation. In this paper, we explore an accurate and explainable COVID-19 diagnosis approach based on human speech, cough, and breath data using the power of machine learning. We first analyze our design space considerations from the data aspect and model aspect. Then, we perform data augmentation, Mel-spectrogram transformation, and develop a deep residual architecture-based model for prediction. Experimental results show that our system outperforms the baseline, with the ROC-AUC result increased by 5.47%. Finally, we perform an interpretation analysis based on the visualization of the activation map to further validate the model.

Li Huining, Chen Xingyu, Qian Xiaoye, Chen Huan, Li Zhengxiong, Bhattacharjee Soumyadeep, Zhang Hanbin, Huang Ming-Chun, Xu Wenyao

2022-Oct-19

Accurate, Acoustic, COVID-19, Explainable

Public Health Public Health

CovidViT: a novel neural network with self-attention mechanism to detect Covid-19 through X-ray images.

In International journal of machine learning and cybernetics

Since the emergence of the novel coronavirus in December 2019, it has rapidly swept across the globe, with a huge impact on daily life, public health and the economy around the world. There is an urgent necessary for a rapid and economical detection method for the Covid-19. In this study, we used the transformers-based deep learning method to analyze the chest X-rays of normal, Covid-19 and viral pneumonia patients. Covid-Vision-Transformers (CovidViT) is proposed to detect Covid-19 cases through X-ray images. CovidViT is based on transformers block with the self-attention mechanism. In order to demonstrate its superiority, this research is also compared with other popular deep learning models, and the experimental result shows CovidViT outperforms other deep learning models and achieves 98.0% accuracy on test set, which means that the proposed model is excellent in Covid-19 detection. Besides, an online system for quick Covid-19 diagnosis is built on http://yanghang.site/covid19.

Yang Hang, Wang Liyang, Xu Yitian, Liu Xuhua

2022-Oct-19

Covid-19, Deep learning, Self-attention, Transformers

General General

A Hybrid Deep Transfer Learning Model With Kernel Metric for COVID-19 Pneumonia Classification Using Chest CT Images.

In IEEE/ACM transactions on computational biology and bioinformatics

Coronavirus disease-2019 (COVID-19) as a new pneumonia which is extremely infectious, the classification of this coronavirus is essential to effectively control the development of the epidemic. Pathological changes in the chest computed tomography (CT) scans are often used as one of the diagnostic criteria of COVID-19. Meanwhile, deep learning-based transfer learning is currently an effective strategy for computer-aided diagnosis (CAD). To further improve the performance of deep transfer learning model used for COVID-19 classification with CT images, in this article, we propose a hybrid model combined with a semi-supervised domain adaption model and extreme learning machine (ELM) classifier, and the application of a novel multikernel correntropy induced loss function in transfer learning is also presented. The proposed model is evaluated on open-source datasets. The experimental results are compared to some baseline models to verify the effectiveness, while adopting accuracy, precision, recall, F1 score and area under curve (AUC) as the evaluation metrics. Experimental results show that the proposed method improves the performance of original model and is more suitable for CT images analysis.

Li Jianyuan, Luo Xiong, Ma Huimin, Zhao Wenbing

2022-Oct-24

General General

Understanding How the Design and Implementation of Online Consultations Affect Primary Care Quality: Systematic Review of Evidence With Recommendations for Designers, Providers, and Researchers.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Online consultations (OCs) allow patients to contact their care providers on the web. Worldwide, OCs have been rolled out in primary care rapidly owing to policy initiatives and COVID-19. There is a lack of evidence regarding how OC design and implementation influence care quality.

OBJECTIVE : We aimed to synthesize research on the impacts of OCs on primary care quality, and how these are influenced by system design and implementation.

METHODS : We searched databases from January 2010 to February 2022. We included quantitative and qualitative studies of real-world OC use in primary care. Quantitative data were transformed into qualitative themes. We used thematic synthesis informed by the Institute of Medicine domains of health care quality, and framework analysis informed by the nonadoption, abandonment, scale-up, spread, and sustainability framework. Strength of evidence was judged using the GRADE-CERQual approach.

RESULTS : We synthesized 63 studies from 9 countries covering 31 OC systems, 14 (22%) of which used artificial intelligence; 41% (26/63) of studies were published from 2020 onward, and 17% (11/63) were published after the COVID-19 pandemic. There was no quantitative evidence for negative impacts of OCs on patient safety, and qualitative studies suggested varied perceptions of their safety. Some participants believed OCs improved safety, particularly when patients could describe their queries using free text. Staff workload decreased when sufficient resources were allocated to implement OCs and patients used them for simple problems or could describe their queries using free text. Staff workload increased when OCs were not integrated with other software or organizational workflows and patients used them for complex queries. OC systems that required patients to describe their queries using multiple-choice questionnaires increased workload for patients and staff. Health costs decreased when patients used OCs for simple queries and increased when patients used them for complex queries. Patients using OCs were more likely to be female, younger, and native speakers, with higher socioeconomic status. OCs increased primary care access for patients with mental health conditions, verbal communication difficulties, and barriers to attending in-person appointments. Access also increased by providing a timely response to patients' queries. Patient satisfaction increased when using OCs owing to better primary care access, although it decreased when using multiple-choice questionnaire formats.

CONCLUSIONS : This is the first theoretically informed synthesis of research on OCs in primary care and includes studies conducted during the COVID-19 pandemic. It contributes new knowledge that, in addition to having positive impacts on care quality such as increased access, OCs also have negative impacts such as increased workload. Negative impacts can be mitigated through appropriate OC system design (eg, free text format), incorporation of advanced technologies (eg, artificial intelligence), and integration into technical infrastructure (eg, software) and organizational workflows (eg, timely responses).

TRIAL REGISTRATION : PROSPERO CRD42020191802; https://tinyurl.com/2p84ezjy.

Darley Sarah, Coulson Tessa, Peek Niels, Moschogianis Susan, van der Veer Sabine N, Wong David C, Brown Benjamin C

2022-Oct-24

COVID-19, OC, care provider, general practice, health care professional, health outcome, pandemic, patient care, primary care, primary health care, remote consultation, systematic review, telemedicine, triage, workforce

Public Health Public Health

Associations between the use of aspirin or other antiplatelet drugs and all-cause mortality among patients with COVID-19: A meta-analysis.

In Frontiers in pharmacology

Introduction: Whether aspirin or other antiplatelet drugs can reduce mortality among patients with coronavirus disease (COVID-19) remains controversial. Methods: We identified randomized controlled trials, prospective cohort studies, and retrospective studies on associations between aspirin or other antiplatelet drug use and all-cause mortality among patients with COVID-19 in the PubMed database between March 2019 and September 2021. Newcastle-Ottawa Scale and Cochrane Risk of Bias Assessment Tool were used to assess the risk of bias. The I2 statistic was used to assess inconsistency among trial results. The summary risk ratio (RR) and odds ratio (OR) were obtained through the meta-analysis. Results: The 34 included studies comprised three randomized controlled trials, 27 retrospective studies, and 4 prospective cohort studies. The retrospective and prospective cohort studies showed low-to-moderate risks of bias per the Newcastle-Ottawa Scale score, while the randomized controlled trials showed low-to-high risks of bias per the Cochrane Risk of Bias Assessment Tool. The randomized controlled trials showed no significant effect of aspirin use on all-cause mortality in patients with COVID-19 {risk ratio (RR), 0.96 [95% confidence interval (CI) 0.90-1.03]}. In retrospective studies, aspirin reduced all-cause mortality in patients with COVID-19 by 20% [odds ratio (OR), 0.80 (95% CI 0.70-0.93)], while other antiplatelet drugs had no significant effects. In prospective cohort studies, aspirin decreased all-cause mortality in patients with COVID-19 by 15% [OR, 0.85 (95% CI 0.80-0.90)]. Conclusion: The administration of aspirin may reduce all-cause mortality in patients with COVID-19.

Su Wanting, Miao He, Guo Zhaotian, Chen Qianhui, Huang Tao, Ding Renyu

2022

COVID-19, antiplatelet drug, aspirin, meta-analysis, mortality

General General

Effect of Disulfide Bridge on the Binding of SARS-CoV-2 Fusion Peptide to Cell Membrane: A Coarse-Grained Study.

In ACS omega

In this paper, we present the parameterization of the CAVS coarse-grained (CG) force field for 20 amino acids, and our CG simulations show that the CAVS force field could accurately predict the amino acid tendency of the secondary structure. Then, we used the CAVS force field to investigate the binding of a severe acute respiratory syndrome-associated coronavirus fusion peptide (SARS-CoV-2 FP) to a phospholipid bilayer: a long FP (FP-L) containing 40 amino acids and a short FP (FP-S) containing 26 amino acids. Our CAVS CG simulations displayed that the binding affinity of the FP-L to the bilayer is higher than that of the FP-S. We found that the FP-L interacted more strongly with membrane cholesterol than the FP-S, which should be attributed to the stable helical structure of the FP-L at the C-terminus. In addition, we discovered that the FP-S had one major and two minor membrane-bound states, in agreement with previous all-atom molecular dynamics (MD) studies. However, we found that both the C-terminal and N-terminal amino acid residues of the FP-L can strongly interact with the bilayer membrane. Furthermore, we found that the disulfide bond formed between Cys840 and Cys851 stabilized the helices of the FP-L at the C-terminus, enhancing the interaction between the FP-L and the bilayer membrane. Our work indicates that the stable helical structure is crucial for binding the SARS-CoV-2 FP to cell membranes. In particular, the helical stability of FP should have a significant influence on the FP-membrane binding.

Shen Hujun, Wu Zhenhua

2022-Oct-18

General General

Ambient air pollutants concentration prediction during the COVID-19: A method based on transfer learning.

In Knowledge-based systems

Research on the correlation analysis between COVID-19 and air pollution has attracted increasing attention since the COVID-19 pandemic. While many relevant issues have been widely studied, research into ambient air pollutant concentration prediction (APCP) during COVID-19 is still in its infancy. Most of the existing study on APCP is based on machine learning methods, which are not suitable for APCP during COVID-19 due to the different distribution of historical observations before and after the pandemic. Therefore, to fulfill the predictive task based on the historical observations with a different distribution, this paper proposes an improved transfer learning model combined with machine learning for APCP during COVID-19. Specifically, this paper employs the Gaussian mixture method and an optimization algorithm to obtain a new source domain similar to the target domain for further transfer learning. Then, several commonly used machine learning models are trained in the new source domain, and these well-trained models are transferred to the target domain to obtain APCP results. Based on the real-world dataset, the experimental results suggest that, by using the improved machine learning methods based on transfer learning, our method can achieve the prediction with significantly high accuracy. In terms of managerial insights, the effects of influential factors are analyzed according to the relationship between these influential factors and prediction results, while their importance is ranked through their average marginal contribution and partial dependence plots.

Chen Shuixia, Xu Zeshui, Wang Xinxin, Zhang Chenxi

2022-Oct-17

Ambient air pollutants concentration prediction, COVID-19, Machine learning, Transfer learning

Radiology Radiology

McS-Net: Multi-class Siamese network for severity of COVID-19 infection classification from lung CT scan slices.

In Applied soft computing

Worldwide COVID-19 is a highly infectious and rapidly spreading disease in almost all age groups. The Computed Tomography (CT) scans of lungs are found to be accurate for the timely diagnosis of COVID-19 infection. In the proposed work, a deep learning-based P-shot N-ways Siamese network along with prototypical nearest neighbor classifiers is implemented for the classification of COVID-19 infection from lung CT scan slices. For this, a Siamese network with an identical sub-network (weight sharing) is used for image classification with a limited dataset for each class. The similarity between the images is determined using nearest neighbor classifiers that use the Euclidean distance between the feature vectors in latent space. The feature vectors are obtained from the pre-trained sub-networks having weight sharing. The performance of the proposed methodology is evaluated on the benchmark MosMed dataset having categories zero (healthy control) and numerous COVID-19 infections, i.e., low infection, intermediate infection, high infection, and extremely high infection. To prove the robustness of the proposed methodology is evaluated on (a) chest CT scans provided by medical hospitals in Moscow, Russia for 1110 patients, and (b) case study of low-dose CT scans of 42 patients provided by Avtaran healthcare in India. The deep learning-based Siamese network (15-shot 5-ways) obtained an accuracy of 98.07%, the sensitivity of 95.66%, specificity of 98.83%, and F1-score of 95.10%. The proposed work outperforms the COVID-19 infection severity classification with limited scans availability for numerous infection categories.

Ahuja Sakshi, Panigrahi Bijaya Ketan, Dey Neelanjan, Taneja Arpit, Gandhi Tapan Kumar

2022-Oct-17

CNN, COVID-19 infection, CT scan, Siamese network

General General

Predicting infectious disease for biopreparedness and response: A systematic review of machine learning and deep learning approaches.

In One health (Amsterdam, Netherlands)

The complex, unpredictable nature of pathogen occurrence has required substantial efforts to accurately predict infectious diseases (IDs). With rising popularity of Machine Learning (ML) and Deep Learning (DL) techniques combined with their unique ability to uncover connections between large amounts of diverse data, we conducted a PRISMA systematic review to investigate advances in ID prediction for human and animal diseases using ML and DL. This review included the type of IDs modeled, ML and DL techniques utilized, geographical distribution, prediction tasks performed, input features utilized, spatial and temporal scales, error metrics used, computational efficiency, uncertainty quantification, and missing data handling methods. Among 237 relevant articles published between January 2001 and May 2021, highly contagious diseases in humans were most often represented, including COVID-19 (37.1%), influenza/influenza-like illnesses (9.3%), dengue (8.9%), and malaria (5.1%). Out of 37 diseases identified, 51.4% were zoonotic, 37.8% were human-only, and 8.1% were animal-only, with only 1.6% economically significant, non-zoonotic livestock diseases. Despite the number of zoonoses, 86.5% of articles modeled humans whereas only a few articles (5.1%) contained more than one host species. Eastern Asia (32.5%), North America (17.7%), and Southern Asia (13.1%) were the most represented locations. Frequent approaches included tree-based ML (38.4%) and feed-forward neural networks (26.6%). Articles predicted temporal incidence (66.7%), disease risk (38.0%), and/or spatial movement (31.2%). Less than 10% of studies addressed uncertainty quantification, computational efficiency, and missing data, which are essential to operational use and deployment. This study highlights trends and gaps in ML and DL for ID prediction, providing guidelines for future works to better support biopreparedness and response. To fully utilize ML and DL for improved ID forecasting, models should include the full disease ecology in a One-Health context, important food and agricultural diseases, underrepresented hotspots, and important metrics required for operational deployment.

Keshavamurthy Ravikiran, Dixon Samuel, Pazdernik Karl T, Charles Lauren E

2022-Dec

Deep learning, Disease forecast, Disease prediction, Infectious diseases, Machine learning, Systematic review

General General

Computationally restoring the potency of a clinical antibody against SARS-CoV-2 Omicron subvariants

bioRxiv Preprint

The COVID-19 pandemic has highlighted how viral variants that escape monoclonal antibodies can limit options to control an outbreak. With the emergence of the SARS-CoV-2 Omicron variant, many clinically used antibody drug products lost in vitro and in vivo potency, including AZD7442 and its constituent, AZD1061. Rapidly modifying such antibodies to restore efficacy to emerging variants is a compelling mitigation strategy. We therefore sought to computationally design an antibody that restores neutralization of BA.1 and BA.1.1 while simultaneously maintaining efficacy against SARS-CoV-2 B.1.617.2 (Delta), beginning from COV2-2130, the progenitor of AZD1061. Here we describe COV2-2130 derivatives that achieve this goal and provide a proof-of-concept for rapid antibody adaptation addressing escape variants. Our best antibody achieves potent and broad neutralization of BA.1, BA.1.1, BA.2, BA.2.12.1, BA.4, BA.5, and BA.5.5 Omicron subvariants, where the parental COV2-2130 suffers significant potency losses. This antibody also maintains potency against Delta and WA1/2020 strains and provides protection in vivo against the strains we tested, WA1/2020, BA.1.1, and BA.5. Because our design approach is computational - driven by high-performance computing-enabled simulation, machine learning, structural bioinformatics and multi-objective optimization algorithms - it can rapidly propose redesigned antibody candidates aiming to broadly target multiple escape variants and virus mutations known or predicted to enable escape.

Desautels, T. A.; Arrildt, K. T.; Zemla, A. T.; Lau, E. Y.; Zhu, F.; Ricci, D.; Cronin, S.; Zost, S.; Binshtein, E.; Scheaffer, S. M.; Engdahl, T. B.; Chen, E.; Goforth, J. W.; Vashchenko, D.; Nguyen, S.; Weilhammer, D. R.; Lo, J. K.-Y.; Rubinfeld, B.; Saada, E. A.; Weisenberger, T.; Lee, T.-H.; Whitener, B.; Case, J. B.; Ladd, A.; Silva, M. S.; Haluska, R. M.; Grzesiak, E. A.; Bates, T. W.; Petersen, B. K.; Thackray, L. B.; Segelke, B. W.; Lillo, A. M.; Sundaram, S.; Diamond, M. S.; Crowe, J. E.; Carnahan, R. H.; Faissol, D. M.

2022-10-24

General General

Effects of long-term COVID-19 confinement and music stimulation on mental state and brain activity of young people.

In Neuroscience letters

The Corona Virus Disease 2019 (COVID-19) pandemic may have had a negative emotional impact on individuals. This study investigated the effect of long-term lockdown and music on young people's mood and neurophysiological responses in the prefrontal cortex (PFC). Fifteen healthy young adults were recruited and PFC activation was acquired using functional near-infrared spectroscopy during the conditions of resting, Stroop and music stimulation. The Depression Anxiety Stress Scales mental scale scores were simultaneously recorded. Mixed effect models, paired t-tests, one-way ANOVAs and Spearman analyses were adopted to analyse the experimental parameters. Stress, anxiety and depression levels increased significantly from Day 30 to Day 40. In terms of reaction time, both Stroop1 and Stroop2 were faster on Day 40 than on Day 30 (P = 0.01, P = 0.003). The relative concentration changes of oxyhemoglobin were significantly higher during premusic conditions than music stimulation and postmusic Stroop. The intensity of functional connectivity shifted from inter- to intracerebral over time. In conclusion, the reduced hemodynamic response of the PFC in healthy young adults is associated with negative emotions, especially anxiety, during lockdown. Immediate music stimulation appears to improve efficiency by altering the pattern of connections in PFC.

Luo Lina, Shan Mianjia, Zu Yangmin, Chen Yufang, Bu Lingguo, Wang Lejun, Ni Ming, Niu Wenxin

2022-Oct-19

Cognitive neuroscience, Functional brain connectivity, Lockdown, Music stimulation, Negative emotions, Prefrontal cortex activation

Public Health Public Health

A storm in a teacup -- A biomimetic lung microphysiological system in conjunction with a deep-learning algorithm to monitor lung pathological and inflammatory reactions.

In Biosensors & bioelectronics

Creating a biomimetic in vitro lung model to recapitulate the infection and inflammatory reactions has been an important but challenging task for biomedical researchers. The 2D based cell culture models - culturing of lung epithelium - have long existed but lack multiple key physiological conditions, such as the involvement of different types of immune cells and the creation of connected lung models to study viral or bacterial infection between different individuals. Pioneers in organ-on-a-chip research have developed lung alveoli-on-a-chip and connected two lung chips with direct tubing and flow. Although this model provides a powerful tool for lung alveolar disease modeling, it still lacks interactions among immune cells, such as macrophages and monocytes, and the mimic of air flow and aerosol transmission between lung-chips is missing. Here, we report the development of an improved human lung physiological system (Lung-MPS) with both alveolar and pulmonary bronchial chambers that permits the integration of multiple immune cells into the system. We observed amplified inflammatory signals through the dynamic interactions among macrophages, epithelium, endothelium, and circulating monocytes. Furthermore, an integrated microdroplet/aerosol transmission system was fabricated and employed to study the propagation of pseudovirus particles containing microdroplets in integrated Lung-MPSs. Finally, a deep-learning algorithm was developed to characterize the activation of cells in this Lung-MPS. This Lung-MPS could provide an improved and more biomimetic sensory system for the study of COVID-19 and other high-risk infectious lung diseases.

Chen Zaozao, Huang Jie, Zhang Jing, Xu Zikang, Li Qiwei, Ouyang Jun, Yan Yuchuan, Sun Shiqi, Ye Huan, Wang Fei, Zhu Jianfeng, Wang Zhangyan, Chao Jie, Pu Yuepu, Gu Zhongze

2022-Oct-01

COVID-19, Cytokine storm, Lung-on-a-chip, Microfluidics

General General

Systematic Review of Advanced AI Methods for Improving Healthcare Data Quality In Post COVID-19 Era.

In IEEE reviews in biomedical engineering

At the beginning of the COVID-19 pandemic, there was significant hype about the potential impact of artificial intelligence (AI) tools in combatting COVID-19 on diagnosis, prognosis, or surveillance. However, AI tools have not yet been widely successful. One of the key reason is the COVID-19 pandemic has demanded faster real-time development of AI-driven clinical and health support tools, including rapid data collection, algorithm development, validation, and deployment. However, there was not enough time for proper data quality control. Learning from the hard lessons in COVID-19, we summarize the important health data quality challenges during COVID-19 pandemic such as lack of data standardization, missing data, tabulation errors, and noise and artifact. Then we conduct a systematic investigation of computational methods that address these issues, including emerging novel advanced AI data quality control methods that achieve better data quality outcomes and, in some cases, simplify or automate the data cleaning process. We hope this article can assist healthcare community to improve health data quality going forward with novel AI development.

Isgut Monica, Gloster Logan, Choi Katherine, Venugopalan Janani, Wang May D

2022-Oct-21

Internal Medicine Internal Medicine

Development and validation of multivariable prediction models of serological response to SARS-CoV-2 vaccination in kidney transplant recipients.

In Frontiers in immunology ; h5-index 100.0

Repeated vaccination against SARS-CoV-2 increases serological response in kidney transplant recipients (KTR) with high interindividual variability. No decision support tool exists to predict SARS-CoV-2 vaccination response to third or fourth vaccination in KTR. We developed, internally and externally validated five different multivariable prediction models of serological response after the third and fourth vaccine dose against SARS-CoV-2 in previously seronegative, COVID-19-naïve KTR. Using 20 candidate predictor variables, we applied statistical and machine learning approaches including logistic regression (LR), least absolute shrinkage and selection operator (LASSO)-regularized LR, random forest, and gradient boosted regression trees. For development and internal validation, data from 590 vaccinations were used. External validation was performed in four independent, international validation cohorts comprising 191, 184, 254, and 323 vaccinations, respectively. LASSO-regularized LR performed on the whole development dataset yielded a 20- and 10-variable model, respectively. External validation showed AUC-ROC of 0.840, 0.741, 0.816, and 0.783 for the sparser 10-variable model, yielding an overall performance 0.812. A 10-variable LASSO-regularized LR model predicts vaccination response in KTR with good overall accuracy. Implemented as an online tool, it can guide decisions whether to modulate immunosuppressive therapy before additional active vaccination, or to perform passive immunization to improve protection against COVID-19 in previously seronegative, COVID-19-naïve KTR.

Osmanodja Bilgin, Stegbauer Johannes, Kantauskaite Marta, Rump Lars Christian, Heinzel Andreas, Reindl-Schwaighofer Roman, Oberbauer Rainer, Benotmane Ilies, Caillard Sophie, Masset Christophe, Kerleau Clarisse, Blancho Gilles, Budde Klemens, Grunow Fritz, Mikhailov Michael, Schrezenmeier Eva, Ronicke Simon

2022

COVID-19, clinical decision support, immunosuppression therapy, kidney transplantation, vaccination

General General

Artificial intelligence assisted acute patient journey.

In Frontiers in artificial intelligence

Artificial intelligence is taking the world by storm and soon will be aiding patients in their journey at the hospital. The trials and tribulations of the healthcare system during the COVID-19 pandemic have set the stage for shifting healthcare from a physical to a cyber-physical space. A physician can now remotely monitor a patient, admitting them only if they meet certain thresholds, thereby reducing the total number of admissions at the hospital. Coordination, communication, and resource management have been core issues for any industry. However, it is most accurate in healthcare. Both systems and providers are exhausted under the burden of increasing data and complexity of care delivery, increasing costs, and financial burden. Simultaneously, there is a digital transformation of healthcare in the making. This transformation provides an opportunity to create systems of care that are artificial intelligence-enabled. Healthcare resources can be utilized more justly. The wastage of financial and intellectual resources in an overcrowded healthcare system can be avoided by implementing IoT, telehealth, and AI/ML-based algorithms. It is imperative to consider the design principles of the patient's journey while simultaneously prioritizing a better user experience to alleviate physician concerns. This paper discusses the entire blueprint of the AI/ML-assisted patient journey and its impact on healthcare provision.

Nazir Talha, Mushhood Ur Rehman Muhammad, Asghar Muhammad Roshan, Kalia Junaid S

2022

AI-based clinical decision support system, Automated EMR summary, acute patient journey, artificial intelligence, electronic-triage, health IoT

General General

Deep learning models-based CT-scan image classification for automated screening of COVID-19.

In Biomedical signal processing and control

COVID-19 is the most transmissible disease, caused by the SARS-CoV-2 virus that severely infects the lungs and the upper respiratory tract of the human body. This virus badly affected the lives and wellness of millions of people worldwide and spread widely. Early diagnosis, timely treatment, and proper confinement of the infected patients are some possible ways to control the spreading of coronavirus. Computed tomography (CT) scanning has proven useful in diagnosing several respiratory lung problems, including COVID-19 infections. Automated detection of COVID-19 using chest CT-scan images may reduce the clinician's load and save the lives of thousands of people. This study proposes a robust framework for the automated screening of COVID-19 using chest CT-scan images and deep learning-based techniques. In this work, a publically accessible CT-scan image dataset (contains the 1252 COVID-19 and 1230 non-COVID chest CT images), two pre-trained deep learning models (DLMs) namely, MobileNetV2 and DarkNet19, and a newly-designed lightweight DLM, are utilized for the automated screening of COVID-19. A repeated ten-fold holdout validation method is utilized for the training, validation, and testing of DLMs. The highest classification accuracy of 98.91% is achieved using transfer-learned DarkNet19. The proposed framework is ready to be tested with more CT images. The simulation results with the publicly available COVID-19 CT scan image dataset are included to show the effectiveness of the presented study.

Gupta Kapil, Bajaj Varun

2023-Feb

COVID-19, CT-scan images, Deep learning, Transfer learning

Internal Medicine Internal Medicine

Machine learning in the diagnosis of asthma phenotypes during coronavirus disease 2019 pandemic.

In Clinical and translational allergy

Background : During the coronavirus disease 2019 (COVID-19) pandemic, it has become a pressing need to be able to diagnose aspirin hypersensitivity in patients with asthma without the need to use oral aspirin challenge (OAC) testing. OAC is time consuming and is associated with the risk of severe hypersensitive reactions. In this study, we sought to investigate whether machine learning (ML) based on some clinical and laboratory procedures performed during the pandemic might be used for discriminating between patients with aspirin hypersensitivity and those with aspirin-tolerant asthma.

Methods : We used a prospective database of 135 patients with non-steroidal anti-inflammatory drug (NSAID)-exacerbated respiratory disease (NERD) and 81 NSAID-tolerant (NTA) patients with asthma who underwent OAC. Clinical characteristics, inflammatory phenotypes based on sputum cells, as well as eicosanoid levels in induced sputum supernatant and urine were extracted for the purpose of applying ML techniques.

Results : The overall best ML model, neural network (NN), trained on a set of best features, achieved a sensitivity of 95% and a specificity of 76% for diagnosing NERD. The 3 promising models (i.e., multiple logistic regression, support vector machine, and NN) trained on a set of easy-to-obtain features including only clinical characteristics and laboratory data achieved a sensitivity of 97% and a specificity of 67%.

Conclusions : ML techniques are becoming a promising tool for discriminating between patients with NERD and NTA. The models are easy to use, safe, and achieve very good results, which is particularly important during the COVID-19 pandemic.

Gawlewicz-Mroczka Agnieszka, Pytlewski Adam, Celejewska-Wójcik Natalia, Ćmiel Adam, Gielicz Anna, Sanak Marek, Mastalerz Lucyna

2022-Oct

COVID‐19 pandemic, machine learning, nonsteroidal anti‐inflammatory drug (NSAID)–exacerbated respiratory disease (NERD), nonsteroidal anti‐inflammatory drug tolerant asthma (NTA), oral aspirin challenge

General General

Smartphone-based Platform Assisted by Artificial Intelligence for Reading and Reporting Rapid Diagnostic Tests: Application to SARS-CoV-2 Lateral Flow Immunoassays.

In JMIR public health and surveillance

BACKGROUND : Rapid diagnostic tests (RDTs) are being widely used to manage COVID-19 pandemic. However, many results remain unreported or unconfirmed altering a correct epidemiological surveillance.

OBJECTIVE : To evaluate an artificial intelligence-based smartphone application, connected to a cloud web platform, to automatically and objectively read rapid diagnostic test (RDT) results and assess its impact on COVID-19 pandemic management.

METHODS : Overall, 252 human sera were used to inoculate a total of 1,165 RDTs for training and validation purposes. We then conducted two field studies to assess the performance on real-world scenarios by testing 172 antibody RDTs at two nursing homes and 96 antigen RDTs at one hospital emergency department.

RESULTS : Field studies demonstrated high levels of sensitivity (100%) and specificity (94.4%, CI 92.8-96.1%) for reading IgG band of COVID-19 antibodies RDTs compared to visual readings from health workers. Sensitivity of detecting IgM test bands was 100% and specificity was 95.8%, CI 94.3-97.3%. All COVID-19 antigen RDTs were correctly read by the app.

CONCLUSIONS : The proposed reading system is automatic, reducing variability and uncertainty associated with RDTs interpretation and can be used to read different RDTs brands. The web platform serves as a real time epidemiological tracking tool and facilitates reporting of positive RDTs to relevant health authorities.

Bermejo-Peláez David, Marcos-Mencía Daniel, Álamo Elisa, Pérez-Panizo Nuria, Mousa Adriana, Dacal Elena, Lin Lin, Vladimirov Alexander, Cuadrado Daniel, Mateos-Nozal Jesús, Galán Juan Carlos, Romero-Hernandez Beatriz, Cantón Rafael, Luengo-Oroz Miguel, Rodriguez-Dominguez Mario

2022-Oct-13

General General

Discovery and analytical validation of a vocal biomarker to monitor anosmia and ageusia in patients with Covid-19: Cross-sectional study.

In JMIR medical informatics ; h5-index 23.0

BACKGROUND : The Covid-19 disease has multiple symptoms, being anosmia, varying from 75-95%, and ageusia, varying from 50-80% of infected patients, the most prevalent ones. An automatic assessment tool for these symptoms will help monitor the disease in a fast and non-invasive manner.

OBJECTIVE : We hypothesized that people with Covid-19 experiencing anosmia and ageusia had different voice features than those without such symptoms. Our objective was to develop an artificial intelligence pipeline to identify and internally validate a vocal biomarker of these symptoms for remotely monitoring them.

METHODS : This study is made on population-based data. Participants were assessed daily through an online questionnaire and asked to register two different types of voice recordings, they were adults (older than 18 years old) that were confirmed by a PCR test to be positive for Covid-19 in Luxembourg and that passed through the exclusion criteria. Statistical methods like Recursive Feature Elimination (RFE) for dimensionality reduction, multiple statistical learning methods, and hypothesis tests were used throughout this study. The TRIPOD Prediction Model Development checklist was used to structure the research.

RESULTS : This study included 259 participants. Young (<35 years old) and females showed a higher rate of ageusia and anosmia. Participants were 41 (SD = 13) years old on average and the dataset was balanced for sex (134 females (52%) and 125 males (48%) out of 259). The analyzed symptom was present in 94 out of 259 (36%) participants of the population and in 450 out of 1636 (28%) audio recordings. Two machine learning models were built, one for Android and one for iOS devices and both had high accuracy, being 88% for Android and 85% for iOS. The final biomarker was then calculated using these models and internally validated.

CONCLUSIONS : This study demonstrates that people with Covid-19 who have anosmia and ageusia have different voice features from those without it. Upon further validation, these vocal biomarkers could be nested in digital devices to improve symptom assessment in clinical practice and enhance telemonitoring of Covid-19-related symptoms.

CLINICALTRIAL : Approved by the National Research Ethics Committee of Luxembourg (study number 202003/07) in April 2020 and is registered Clinicaltrials.gov NCT04380987, https://clinicaltrials.gov/ct2/show/NCT04380987.

Higa Eduardo, Zhang Lu, Elbéji Abir, Fischer Aurélie, Aguayo Gloria A, Nazarov Petr V, Fagherazzi Guy

2022-Sep-07

General General

Evaluation of digital economy development level based on multi-attribute decision theory.

In PloS one ; h5-index 176.0

The maturity and commercialization of emerging digital technologies represented by artificial intelligence, cloud computing, block chain and virtual reality are giving birth to a new and higher economic form, that is, digital economy. Digital economy is different from the traditional industrial economy. It is clean, efficient, green and recyclable. It represents and promotes the future direction of global economic development, especially in the context of the sudden COVID-19 pandemic as a continuing disaster. Therefore, it is essential to establish the comprehensive evaluation model of digital economy development scientifically and reasonably. In this paper, first on the basis of literature analysis, the relevant indicators of digital economy development are collected manually and then screened by the grey dynamic clustering and rough set reduction theory. The evaluation index system of digital economy development is constructed from four dimensions: digital innovation impetus support, digital infrastructure construction support, national economic environment and digital policy guarantee, digital integration and application. Next the subjective weight and objective weight are calculated by the group FAHP method, entropy method and improved CRITIC method, and the combined weight is integrated with the thought of maximum variance. The grey correlation analysis and improved VIKOR model are combined to systematically evaluate the digital economy development level of 31 provinces and cities in China from 2013 to 2019. The results of empirical analysis show that the overall development of China's digital economy shows a trend of superposition and rise, and the development of digital economy in the four major economic zones is unbalanced. Finally, we put forward targeted opinions on the construction of China's provincial digital economy.

Su Jinqi, Su Ke, Wang Shubin

2022

General General

Predicting responders to prone positioning in mechanically ventilated patients with COVID-19 using machine learning.

In Annals of intensive care ; h5-index 37.0

BACKGROUND : For mechanically ventilated critically ill COVID-19 patients, prone positioning has quickly become an important treatment strategy, however, prone positioning is labor intensive and comes with potential adverse effects. Therefore, identifying which critically ill intubated COVID-19 patients will benefit may help allocate labor resources.

METHODS : From the multi-center Dutch Data Warehouse of COVID-19 ICU patients from 25 hospitals, we selected all 3619 episodes of prone positioning in 1142 invasively mechanically ventilated patients. We excluded episodes longer than 24 h. Berlin ARDS criteria were not formally documented. We used supervised machine learning algorithms Logistic Regression, Random Forest, Naive Bayes, K-Nearest Neighbors, Support Vector Machine and Extreme Gradient Boosting on readily available and clinically relevant features to predict success of prone positioning after 4 h (window of 1 to 7 h) based on various possible outcomes. These outcomes were defined as improvements of at least 10% in PaO2/FiO2 ratio, ventilatory ratio, respiratory system compliance, or mechanical power. Separate models were created for each of these outcomes. Re-supination within 4 h after pronation was labeled as failure. We also developed models using a 20 mmHg improvement cut-off for PaO2/FiO2 ratio and using a combined outcome parameter. For all models, we evaluated feature importance expressed as contribution to predictive performance based on their relative ranking.

RESULTS : The median duration of prone episodes was 17 h (11-20, median and IQR, N = 2632). Despite extensive modeling using a plethora of machine learning techniques and a large number of potentially clinically relevant features, discrimination between responders and non-responders remained poor with an area under the receiver operator characteristic curve of 0.62 for PaO2/FiO2 ratio using Logistic Regression, Random Forest and XGBoost. Feature importance was inconsistent between models for different outcomes. Notably, not even being a previous responder to prone positioning, or PEEP-levels before prone positioning, provided any meaningful contribution to predicting a successful next proning episode.

CONCLUSIONS : In mechanically ventilated COVID-19 patients, predicting the success of prone positioning using clinically relevant and readily available parameters from electronic health records is currently not feasible. Given the current evidence base, a liberal approach to proning in all patients with severe COVID-19 ARDS is therefore justified and in particular regardless of previous results of proning.

Dam Tariq A, Roggeveen Luca F, van Diggelen Fuda, Fleuren Lucas M, Jagesar Ameet R, Otten Martijn, de Vries Heder J, Gommers Diederik, Cremer Olaf L, Bosman Rob J, Rigter Sander, Wils Evert-Jan, Frenzel Tim, Dongelmans Dave A, de Jong Remko, Peters Marco A A, Kamps Marlijn J A, Ramnarain Dharmanand, Nowitzky Ralph, Nooteboom Fleur G C A, de Ruijter Wouter, Urlings-Strop Louise C, Smit Ellen G M, Mehagnoul-Schipper D Jannet, Dormans Tom, de Jager Cornelis P C, Hendriks Stefaan H A, Achterberg Sefanja, Oostdijk Evelien, Reidinga Auke C, Festen-Spanjer Barbara, Brunnekreef Gert B, Cornet Alexander D, van den Tempel Walter, Boelens Age D, Koetsier Peter, Lens Judith, Faber Harald J, Karakus A, Entjes Robert, de Jong Paul, Rettig Thijs C D, Arbous Sesmu, Vonk Sebastiaan J J, Machado Tomas, Herter Willem E, de Grooth Harm-Jan, Thoral Patrick J, Girbes Armand R J, Hoogendoorn Mark, Elbers Paul W G

2022-Oct-20

Acute respiratory distress syndrome, COVID-19, Mechanical ventilation

General General

Serological responses to human virome define clinical outcomes of Italian patients infected with SARS-CoV-2.

In International journal of biological sciences

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the pandemic respiratory infectious disease COVID-19. However, clinical manifestations and outcomes differ significantly among COVID-19 patients, ranging from asymptomatic to extremely severe, and it remains unclear what drives these disparities. Here, we studied 159 sequentially enrolled hospitalized patients with COVID-19-associated pneumonia from Brescia, Italy using the VirScan phage-display method to characterize circulating antibodies binding to 96,179 viral peptides encoded by 1,276 strains of human viruses. SARS-CoV-2 infection was associated with a marked increase in immune antibody repertoires against many known pathogenic and non-pathogenic human viruses. This antiviral antibody response was linked to longitudinal trajectories of disease severity and was further confirmed in additional 125 COVID-19 patients from the same geographical region in Northern Italy. By applying a machine-learning-based strategy, a viral exposure signature predictive of COVID-19-related disease severity linked to patient survival was developed and validated. These results provide a basis for understanding the role of memory B-cell repertoire to viral epitopes in COVID-19-related symptoms and suggest that a unique anti-viral antibody repertoire signature may be useful to define COVID-19 clinical severity.

Wang Limin, Candia Julián, Ma Lichun, Zhao Yongmei, Imberti Luisa, Sottini Alessandra, Quiros-Roldan Eugenia, Dobbs Kerry, Burbelo Peter D, Cohen Jeffrey I, Delmonte Ottavia M, Forgues Marshonna, Liu Hui, Matthews Helen F, Shaw Elana, Stack Michael A, Weber Sarah E, Zhang Yu, Lisco Andrea, Sereti Irini, Su Helen C, Notarangelo Luigi D, Wang Xin Wei

2022

General General

COVID19 Diagnosis Using Chest X-rays and Transfer Learning.

In medRxiv : the preprint server for health sciences

** : A pandemic of respiratory illnesses from a novel coronavirus known as Sars-CoV-2 has swept across the globe since December of 2019. This is calling upon the research community including medical imaging to provide effective tools for use in combating this virus. Research in biomedical imaging of viral patients is already very active with machine learning models being created for diagnosing Sars-CoV-2 infections in patients using CT scans and chest x-rays. We aim to build upon this research. Here we used a transfer-learning approach to develop models capable of diagnosing COVID19 from chest x-ray. For this work we compiled a dataset of 112120 negative images from the Chest X-Ray 14 and 2725 positive images from public repositories. We tested multiple models, including logistic regression and random forest and XGBoost with and without principal components analysis, using five-fold cross-validation to evaluate recall, precision, and f1-score. These models were compared to a pre-trained deep-learning model for evaluating chest x-rays called COVID-Net. Our best model was XGBoost with principal components with a recall, precision, and f1-score of 0.692, 0.960, 0.804 respectively. This model greatly outperformed COVID-Net which scored 0.987, 0.025, 0.048. This model, with its high precision and reasonable sensitivity, would be most useful as "rule-in" test for COVID19. Though it outperforms some chemical assays in sensitivity, this model should be studied in patients who would not ordinarily receive a chest x-ray before being used for screening.

CCS CONCEPTS : Life and Medical Sciences • Machine Learning • Artificial Intelligence.

Reference format : Jonathan Stubblefield, Jason Causey, Dakota Dale, Jake Qualls, Emily Bellis, Jennifer Fowler, Karl Walker and Xiuzhen Huang. 2022. COVID19 Diagnosis Using Chest X-Rays and Transfer Learning.

Stubblefield Jonathan, Causey Jason, Dale Dakota, Qualls Jake, Bellis Emily, Fowler Jennifer, Walker Karl, Huang Xiuzhen

2022-Oct-12

General General

Distinct responses of newly identified monocyte subsets to advanced gastrointestinal cancer and COVID-19.

In Frontiers in immunology ; h5-index 100.0

Monocytes are critical cells of the immune system but their role as effectors is relatively poorly understood, as they have long been considered only as precursors of tissue macrophages or dendritic cells. Moreover, it is known that this cell type is heterogeneous, but our understanding of this aspect is limited to the broad classification in classical/intermediate/non-classical monocytes, commonly based on their expression of only two markers, i.e. CD14 and CD16. We deeply dissected the heterogeneity of human circulating monocytes in healthy donors by transcriptomic analysis at single-cell level and identified 9 distinct monocyte populations characterized each by a profile suggestive of specialized functions. The classical monocyte subset in fact included five distinct populations, each enriched for transcriptomic gene sets related to either inflammatory, neutrophil-like, interferon-related, and platelet-related pathways. Non-classical monocytes included two distinct populations, one of which marked specifically by elevated expression levels of complement components. Intermediate monocytes were not further divided in our analysis and were characterized by high levels of human leukocyte antigen (HLA) genes. Finally, we identified one cluster included in both classical and non-classical monocytes, characterized by a strong cytotoxic signature. These findings provided the rationale to exploit the relevance of newly identified monocyte populations in disease evolution. A machine learning approach was developed and applied to two single-cell transcriptome public datasets, from gastrointestinal cancer and Coronavirus disease 2019 (COVID-19) patients. The dissection of these datasets through our classification revealed that patients with advanced cancers showed a selective increase in monocytes enriched in platelet-related pathways. Of note, the signature associated with this population correlated with worse prognosis in gastric cancer patients. Conversely, after immunotherapy, the most activated population was composed of interferon-related monocytes, consistent with an upregulation in interferon-related genes in responder patients compared to non-responders. In COVID-19 patients we confirmed a global activated phenotype of the entire monocyte compartment, but our classification revealed that only cytotoxic monocytes are expanded during the disease progression. Collectively, this study unravels an unexpected complexity among human circulating monocytes and highlights the existence of specialized populations differently engaged depending on the pathological context.

Rigamonti Alessandra, Castagna Alessandra, Viatore Marika, Colombo Federico Simone, Terzoli Sara, Peano Clelia, Marchesi Federica, Locati Massimo

2022

COVID-19, cancer, immunotherapy, machine learning, monocyte, single-cell transcriptome

General General

Detection of COVID-19 using deep learning on x-ray lung images.

In PeerJ. Computer science

COVID-19 is a widespread deadly virus that directly affects the human lungs. The spread of COVID-19 did not stop at humans but also reached animals, so it was necessary to limit it is spread and diagnose cases quickly by applying a quarantine to the infected people. Recently x-ray lung images are used to determine the infection and from here the idea of this research came to use deep learning techniques to analyze x-ray lung images publicly available on Kaggle to possibly detect COVID-19 infection. In this article, we have proposed a method to possibly detect the COVID-19 by analyzing the X-ray images and applying a number of deep learning pre-trained models such as InceptionV3, DenseNet121, ResNet50, and VGG16, and the results are compared to determine the best performance model and accuracy with the least loss for our dataset. Our evaluation results showed that the best performing model for our dataset is ResNet50 with accuracies of 99.99%, 99.50%, and 99.44% for training, validation, and testing respectively followed by DenseNet121, InceptionV3, and finally VGG16.

Odeh AbdAlRahman, Alomar Ayah, Aljawarneh Shadi

2022

COVID-19, Classification, Deep learning, Supervised learning, Transfer learning

General General

Analyzing perceptions of a global event using CNN-LSTM deep learning approach: the case of Hajj 1442 (2021).

In PeerJ. Computer science

Hajj (pilgrimage) is a unique social and religious event in which many Muslims worldwide come to perform Hajj. More than two million people travel to Makkah, Saudi Arabia annually to perform various Hajj rituals for four to five days. However, given the recent outbreak of the coronavirus (COVID-19) and its variants, Hajj in the last 2 years 2020-2021 has been different because pilgrims were limited down to a few thousand to control and prevent the spread of COVID-19. This study employs a deep learning approach to investigate the impressions of pilgrims and others from within and outside the Makkah community during the 1442 AH Hajj season. Approximately 4,300 Hajj-related posts and interactions were collected from social media channels, such as Twitter and YouTube, during the Hajj season Dhul-Hijjah 1-13, 1442 (July 11-23, 2021). Convolutional neural networks (CNNs) and long short-term memory (LSTM) deep learning methods were utilized to investigate people's impressions from the collected data. The CNN-LSTM approach showed superior performance results compared with other widely used classification models in terms of F-score and accuracy. Findings revealed significantly positive sentiment rates for tweets collected from Mina and Arafa holy sites, with ratios exceeding 4 out of 5. Furthermore, the sentiment analysis (SA) rates for tweets about Hajj and pilgrims varied during the days of Hajj. Some were classified as positive tweets, such as describing joy at receiving the days of Hajj, and some were negative tweets, such as expressing the impression about the hot weather and the level of satisfaction for some services. Moreover, the SA of comments on several YouTube videos revealed positive classified comments, including praise and supplications, and negative classified comments, such as expressing regret that the Hajj was limited to a small number of pilgrims.

Shambour Mohd Khaled

2022

Convolutional Neural Networks (CNN), Deep learning, Hajj rituals, Long short term memory, Sentiment analysis

Public Health Public Health

Machine Learning Techniques to Explore Clinical Presentations of COVID-19 Disease Severity and to Test the Association with Unhealthy Opioid Use (UOU): Retrospective Cross-sectional Cohort Study.

In JMIR public health and surveillance

BACKGROUND : The COVID-19 pandemic has exacerbated health inequities in the United States. People with unhealthy opioid use (UOU) may face disproportionate challenges with COVID-19 precautions, and the pandemic has disrupted access to opioids and UOU treatments. Unhealthy opioid use impairs the immunological, cardiovascular, pulmonary, renal, and neurological systems and may increase severity of outcomes for COVID-19.

OBJECTIVE : To apply machine learning techniques in order to explore clinical presentations of hospitalized patients with UOU and COVID-19 and to test the association between UOU and COVID-19 disease severity.

METHODS : This retrospective, cross-sectional cohort study was conducted based on data from 4,110 electronic health record patient encounters at an academic health center in Chicago between January 1, 2020, and December 31, 2020. Inclusion criteria were unplanned admissions for patients ≥18 years of age; encounters were counted as COVID-19-positive if there was a positive test for COVID-19 or two COVID-19 ICD-10 codes recorded in the encounter. Using a predefined cutoff with optimal sensitivity and specificity to identify UOU, we ran a machine learning UOU classifier on the data for patients with COVID-19 to estimate the subcohort of patients with UOU. Topic modeling was used to explore and compare the clinical presentations documented for two subgroups: encounters with UOU and COVID-19 and those with no-UOU and COVID-19. Mixed effects logistic regression accounted for multiple encounters for some patients and tested the association between UOU and COVID-19 outcome severity. Severity was measured with three utilization metrics: low - unplanned admission, medium - unplanned admission and receiving mechanical ventilation, and high - unplanned admission with in-hospital death. All models controlled for age, sex, race/ethnicity, insurance status, and body mass index (BMI).

RESULTS : Topic modeling yielded ten topics per subgroup and highlighted unique comorbidities associated with UOU and COVID-19 (e.g., HIV) and no-UOU and COVID-19 (e.g., diabetes). In regression analysis, each incremental increase in the classifier's predicted probability of UOU was associated with 1.16 higher odds of COVID-19 outcome severity (odds ratio 1.16, 95% CI 1.04-1.29, P=.009).

CONCLUSIONS : Among patients hospitalized with COVID-19, UOU is an independent risk factor associated with greater outcome severity, including in-hospital death. Social determinants of health and opioid-related overdose are unique comorbidities in the clinical presentation of the UOU patient subgroup. Additional research is needed on the role of COVID-19 therapeutics and inpatient management of acute COVID-19 pneumonia for patients with UOU. Further research is needed to test associations between expanded evidence-based harm reduction strategies for UOU and vaccination rates, hospitalizations, and risks for overdose and death among people with UOU and COVID-19. Machine learning techniques may offer more exhaustive means for cohort discovery and a novel mixed methods approach to population health.

CLINICALTRIAL :

Thompson Hale M, Sharma Brihat, Smith Dale, Bhalla Sameer, Erondu Ihuoma, Hazra Aniruddha, Ilyas Yousaf, Pachwicewicz Paul, Sheth Neeral K, Chhabra Neeraj, Karnik Niranjan S, Afshar Majid

2022-Oct-18

Pathology Pathology

A prediction model for COVID-19 liver dysfunction in patients with normal hepatic biochemical parameters.

In Life science alliance

Coronavirus disease 2019 (COVID-19) patients with liver dysfunction (LD) have a higher chance of developing severe and critical disease. The routine hepatic biochemical parameters ALT, AST, GGT, and TBIL have limitations in reflecting COVID-19-related LD. In this study, we performed proteomic analysis on 397 serum samples from 98 COVID-19 patients to identify new biomarkers for LD. We then established 19 simple machine learning models using proteomic measurements and clinical variables to predict LD in a development cohort of 74 COVID-19 patients with normal hepatic biochemical parameters. The model based on the biomarker ANGL3 and sex (AS) exhibited the best discrimination (time-dependent AUCs: 0.60-0.80), calibration, and net benefit in the development cohort, and the accuracy of this model was 69.0-73.8% in an independent cohort. The AS model exhibits great potential in supporting optimization of therapeutic strategies for COVID-19 patients with a high risk of LD. This model is publicly available at https://xixihospital-liufang.shinyapps.io/DynNomapp/.

Bao Jianfeng, Liu Shourong, Liang Xiao, Wang Congcong, Cao Lili, Li Zhaoyi, Wei Furong, Fu Ai, Shi Yingqiu, Shen Bo, Zhu Xiaoli, Zhao Yuge, Liu Hong, Miao Liangbin, Wang Yi, Liang Shuang, Wu Linyan, Huang Jinsong, Guo Tiannan, Liu Fang

2023-Jan

General General

Role of Technology in Detection of COVID-19.

In Cureus

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus caused coronavirus infection termed as COVID-19, an illness that has spread devastation all over the world. It was developed first in China and had swiftly spread throughout the world. COVID has created imposed burden on health in the lives of all individuals around the globe. This article provides a number of unprecedented detection technologies used in the detection of infection. COVID has created a large number of symptoms in the young, adolescent as well as elderly population. Old age people are susceptible to fatal serious symptoms because of low immunity. With these goals in mind, this article includes substantial condemning descriptions of the majority of initiatives in order to create diagnostic tools for easy diagnosis. It also provides the reader with a multidisciplinary viewpoint on how traditional approaches such as serology and reverse transcriptase polymerase chain reaction (RT-PCR) along with the frontline techniques such as clustered regularly interspaced short palindromic repeats (CRISPR)/Cas and artificial intelligence/machine learning have been utilized to gather information. The story will inspire creative new ways for successful detection therapy and to prevent this pandemic among a wide audience of operating and aspiring biomedical scientists and engineers.

Lohiya Drishti V, Pathak Swanand S

2022-Sep

artificial intelligence, cas system, covid, ct-scan, elisa, molecular investigations, rt-pcr, sars-cov-2, serological test, thermometer thermal scanners

General General

Proximity Detection During Epidemics: Direct UWB TOA Versus Machine Learning Based RSSI.

In International journal of wireless information networks

** : In this paper, we compare the direct TOA-based UWB technology with the RSSI-based BLE technology using machine learning algorithms for proximity detection during epidemics in terms of complexity of implementation, availability in existing smart phones, and precision of the results. We establish the theoretical limits on the precision and confidence of proximity estimation for both technologies using the Cramer Rao Lower Bound (CRLB) and validate the theoretical foundations using empirical data gathered in diverse practical operating scenarios. We perform our empirical experiments at eight distances in three flat environments and one non-flat environment encompassing both Line of Sight (LOS) and Obstructed-LOS (OLOS) situations. We also analyze the effects of various postures (eight angles) of the person carrying the sensor, and four on-body locations of the sensor. To estimate the range with BLE RSSI, we use 14 features for training the Gradient Boosted Machines (GBM) learning algorithm and we compare the precision of results with those obtained from memoryless UWB TOA ranging algorithm. We show that the memoryless UWB TOA algorithm achieves 93.60% confidence, slightly outperforming the 92.85% confidence of the BLE RSSI with more complex GBM machine learning (ML) algorithm and the need for substantial training. The training process for the RSSI-based BLE social distance measurements involved 3000 measurements to create a training dataset for each scenario and post-processing of data to extract 14 features of RSSI, and the ML classification algorithm consumed 200 s of computational time. The memoryless UWB ranging algorithm achieves more robust results without any need for training in less than 0.5 s of computation time.

Graphical Abstract :

Su Zhuoran, Pahlavan Kaveh, Agu Emmanuel, Wei Haowen

2022-Oct-14

BLE, COVID-19, Classical estimation theory, Proximity detection, RSSI features, UWB

Public Health Public Health

Application of machine learning approaches to predict the impact of ambient air pollution on outpatient visits for acute respiratory infections.

In The Science of the total environment

With a remarkable increase in industrialization among fast-developing countries, air pollution is rising at an alarming rate and has become a public health concern. The study aims to examine the effect of air pollution on patient's hospital visits for respiratory diseases, particularly Acute Respiratory Infections (ARI). Outpatient hospital visits, air pollution and meteorological parameters were collected from March 2018 to October 2021. Eight machine learning algorithms (Random Forest model, K-Nearest Neighbors regression model, Linear regression model, LASSO regression model, Decision Tree Regressor, Support Vector Regression, X.G. Boost and Deep Neural Network with 5-layers) were applied for the analysis of daily air pollutants and outpatient visits for ARI. The evaluation was done by using 5-cross-fold confirmations. The data was randomly divided into test and training data sets at a scale of 1:2, respectively. Results show that among the studied eight machine learning models, the Random Forest model has given the best performance with R2 = 0.606, 0.608 without lag and 1-day lag respectively on ARI patients and R2 = 0.872, 0.871 without lag and 1-day lag respectively on total patients. All eight models did not perform well with the lag effect on the ARI patient dataset but performed better on the total patient dataset. Thus, the study did not find any significant association between ARI patients and ambient air pollution due to the intermittent availability of data during the COVID-19 period. This study gives insight into developing machine learning programs for risk prediction that can be used to predict analytics for several other diseases apart from ARI, such as heart disease and other respiratory diseases.

Ravindra Khaiwal, Bahadur Samsher Singh, Katoch Varun, Bhardwaj Sanjeev, Kaur-Sidhu Maninder, Gupta Madhu, Mor Suman

2022-Oct-15

ARI, Air pollution, Machine learning programs, Random forest regression, Risk prediction

General General

Active deep learning from a noisy teacher for semi-supervised 3D image segmentation: Application to COVID-19 pneumonia infection in CT.

In Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society

Supervised deep learning has become a standard approach to solving medical image segmentation tasks. However, serious difficulties in attaining pixel-level annotations for sufficiently large volumetric datasets in real-life applications have highlighted the critical need for alternative approaches, such as semi-supervised learning, where model training can leverage small expert-annotated datasets to enable learning from much larger datasets without laborious annotation. Most of the semi-supervised approaches combine expert annotations and machine-generated annotations with equal weights within deep model training, despite the latter annotations being relatively unreliable and likely to affect model optimization negatively. To overcome this, we propose an active learning approach that uses an example re-weighting strategy, where machine-annotated samples are weighted (i) based on the similarity of their gradient directions of descent to those of expert-annotated data, and (ii) based on the gradient magnitude of the last layer of the deep model. Specifically, we present an active learning strategy with a query function that enables the selection of reliable and more informative samples from machine-annotated batch data generated by a noisy teacher. When validated on clinical COVID-19 CT benchmark data, our method improved the performance of pneumonia infection segmentation compared to the state of the art.

Hussain Mohammad Arafat, Mirikharaji Zahra, Momeny Mohammad, Marhamati Mahmoud, Neshat Ali Asghar, Garbi Rafeef, Hamarneh Ghassan

2022-Oct-07

Active learning, COVID-19, Deep learning, Noisy teacher, Pneumonia, Segmentation, Semi-supervised learning

Public Health Public Health

Exploring the influence of COVID-19 on the spread of hand, foot, and mouth disease with an automatic machine learning prediction model.

In Environmental science and pollution research international

Hand, foot, and mouth disease (HFMD) is an important public health problem and has received concern worldwide. Moreover, the coronavirus disease 2019 (COVID-19) epidemic also increases the difficulty of understanding and predicting the prevalence of HFMD. The purpose is to prove the usability and applicability of the automatic machine learning (Auto-ML) algorithm in predicting the epidemic trend of HFMD and to explore the influence of COVID-19 on the spread of HFMD. The AutoML algorithm and the autoregressive integrated moving average (ARIMA) model were applied to construct and validate models, based on the monthly incidence numbers of HFMD and meteorological factors from May 2008 to December 2019 in Henan province, China. A total of four models were established, among which the Auto-ML model with meteorological factors had minimum RMSE and MAE in both the model constructing phase and forecasting phase (training set: RMSE = 1424.40 and MAE = 812.55; test set: RMSE = 2107.83, MAE = 1494.41), so this model has the best performance. The optimal model was used to further predict the incidence numbers of HFMD in 2020 and then compared with the reported cases. And, for analysis, 2020 was divided into two periods. The predicted incidence numbers followed the same trend as the reported cases of HFMD before the COVID-19 outbreak; while after the COVID-19 outbreak, the reported cases have been greatly reduced than expected, with an average of only about 103 cases per month, and the incidence peak has also been delayed, which has led to significant changes in the seasonality of HFMD. Overall, the AutoML algorithm is an applicable and ideal method to predict the epidemic trend of the HFMD. Furthermore, it was found that the countermeasures of COVID-19 have a certain influence on suppressing the spread of HFMD during the period of COVID-19. The findings are helpful to health administrative departments.

Yang Chuan, An Shuyi, Qiao Baojun, Guan Peng, Huang Desheng, Wu Wei

2022-Oct-18

Automatic machine learning, COVID-19, Countermeasures, HFMD, Prediction, Time series

General General

Clinical and Temporal Characterization of COVID-19 Subgroups Using Patient Vector Embeddings of Electronic Health Records.

In Journal of the American Medical Informatics Association : JAMIA

OBJECTIVE : To identify and characterize clinical subgroups of hospitalized COVID-19 patients.

MATERIALS AND METHODS : Electronic health records of hospitalized COVID-19 patients at NewYork-Presbyterian/Columbia University Irving Medical Center were temporally sequenced and transformed into patient vector representations using Paragraph Vector models. K-means clustering was performed to identify subgroups.

RESULTS : A diverse cohort of 11,313 patients with COVID-19 and hospitalizations between March 2, 2020 and December 1, 2021 were identified; median [IQR] age: 61.2 [40.3-74.3]; 51.5% female. Twenty subgroups of hospitalized COVID-19 patients, labeled by increasing severity, were characterized by their demographics, conditions, outcomes, and severity (mild-moderate/severe/critical). Subgroup temporal patterns were characterized by the durations in each subgroup, transitions between subgroups, and the complete paths throughout the course of hospitalization.

DISCUSSION : Several subgroups had mild-moderate SARS-CoV-2 infections but were hospitalized for underlying conditions (pregnancy, cardiovascular disease (CVD), etc.). Subgroup 7 included solid organ transplant recipients who mostly developed mild-moderate or severe disease. Subgroup 9 had a history of type-2 diabetes, kidney and CVD, and suffered the highest rates of heart failure (45.2%) and end-stage renal disease (80.6%). Subgroup 13 was the oldest (median: 82.7 years) and had mixed severity but high mortality (33.3%). Subgroup 17 had critical disease and the highest mortality (64.6%), with age (median: 68.1 years) being the only notable risk factor. Subgroups 18-20 had critical disease with high complication rates and long hospitalizations (median: 40+ days). All subgroups are detailed in the full text. A chord diagram depicts the most common transitions, and paths with the highest prevalence, longest hospitalizations, lowest and highest mortalities are presented. Understanding these subgroups and their pathways may aid clinicians in their decisions for better management and earlier intervention for patients.

Ta Casey N, Zucker Jason E, Chiu Po-Hsiang, Fang Yilu, Natarajan Karthik, Weng Chunhua

2022-Oct-18

COVID-19, Cluster analysis, SARS-CoV-2, Unsupervised machine learning

Public Health Public Health

Public Willingness to Engage With COVID-19 Contact Tracing, Quarantine, and Exposure Notification.

In Public health reports (Washington, D.C. : 1974)

OBJECTIVES : We conducted a survey to understand how people's willingness to share information with contact tracers, quarantine after a COVID-19 exposure, or activate and use a smartphone exposure notification (EN) application (app) differed by the person or organization making the request or recommendation.

METHODS : We analyzed data from a nationally representative survey with hypothetical scenarios asking participants (N = 2157) to engage in a public health action by health care providers, public health departments, employers, and others. We used Likert scales and ordered logistic regression to compare willingness to take action based on which person or organization made the request, and we summarized findings by race and ethnicity.

RESULTS : The highest levels of willingness to engage in contact tracing (adjusted odds ratio [aOR] = 1.74; 95% CI, 1.55-1.96), quarantine (aOR = 1.91; 95% CI, 1.69-2.15), download/activate an EN app (aOR = 1.30; 95% CI, 1.16-1.46), and notify other EN users (aOR = 1.43; 95% CI, 1.27-1.60) were reported when the request came from the participant's personal health care provider rather than from federal public health authorities. When compared with non-Hispanic White participants, non-Hispanic Black participants reported significantly higher levels of willingness to engage in contact tracing (aOR = 1.32; 95% CI, 1.18-1.48), quarantine (aOR = 1.49; 95% CI, 1.37-1.63), download/activate an EN app (aOR = 2.19; 95% CI, 2.01-2.38), and notify other EN users (aOR = 1.63; 95% CI, 1.49-1.79).

CONCLUSIONS : Partnering with individuals and organizations perceived as trustworthy may help influence people expressing a lower level of willingness to engage in each activity, while those expressing a higher level of willingness to engage in each activity may benefit from targeted communications.

Liccardi Ilaria, Alekseyev Jesslyn, Woltz Vilhelm L Andersen, McLean Jody E, Zurko Mary Ellen

2022-Oct-18

COVID-19, attitudes, contact tracing, exposure notification, health knowledge, practice, quarantine

Public Health Public Health

Struggling With Recovery From Opioids: Who Is at Risk During COVID-19?

In Journal of addiction medicine ; h5-index 27.0

OBJECTIVES : Individuals in recovery from opioid use disorder (OUD) are vulnerable to the impacts of the COVID-19 pandemic. Recent findings suggest increased relapse risk and overdose linked to COVID-19-related stressors. We aimed to identify individual-level factors associated with COVID-19-related impacts on recovery.

METHODS : This observational study (NCT04577144) enrolled 216 participants who previously partook in long-acting buprenorphine subcutaneous injection clinical trials (2015-2017) for OUD. Participants indicated how COVID-19 affected their recovery from substance use. A machine learning approach Classification and Regression Tree analysis examined the association of 28 variables with the impact of COVID-19 on recovery, including demographics, substance use, and psychosocial factors. Tenfold cross-validation was used to minimize overfitting.

RESULTS : Twenty-six percent of the sample reported that COVID-19 had made recovery somewhat or much harder. Past-month opioid use was higher among those who reported that recovery was harder compared with those who did not (51% vs 24%, respectively; P < 0.001). The final classification tree (overall accuracy, 80%) identified the Beck Depression Inventory (BDI-II) as the strongest independent risk factor associated with reporting COVID-19 impact. Individuals with a BDI-II score ≥10 had 6.45 times greater odds of negative impact (95% confidence interval, 3.29-13.30) relative to those who scored <10. Among individuals with higher BDI-II scores, less progress in managing substance use and treatment of OUD within the past 2 to 3 years were also associated with negative impacts.

CONCLUSIONS : These findings underscore the importance of monitoring depressive symptoms and perceived progress in managing substance use among those in recovery from OUD, particularly during large-magnitude crises.

Keith Diana R, Tegge Allison N, Stein Jeffrey S, Athamneh Liqa N, Craft William H, Chilcoat Howard D, Le Moigne Anne, DeVeaugh-Geiss Angela, Bickel Warren K

2022-Oct-18

Pathology Pathology

Robustness of Demonstration-based Learning Under Limited Data Scenario

ArXiv Preprint

Demonstration-based learning has shown great potential in stimulating pretrained language models' ability under limited data scenario. Simply augmenting the input with some demonstrations can significantly improve performance on few-shot NER. However, why such demonstrations are beneficial for the learning process remains unclear since there is no explicit alignment between the demonstrations and the predictions. In this paper, we design pathological demonstrations by gradually removing intuitively useful information from the standard ones to take a deep dive of the robustness of demonstration-based sequence labeling and show that (1) demonstrations composed of random tokens still make the model a better few-shot learner; (2) the length of random demonstrations and the relevance of random tokens are the main factors affecting the performance; (3) demonstrations increase the confidence of model predictions on captured superficial patterns. We have publicly released our code at https://github.com/SALT-NLP/RobustDemo.

Hongxin Zhang, Yanzhe Zhang, Ruiyi Zhang, Diyi Yang

2022-10-19

General General

Retraction Note: An adaptive speech signal processing for COVID-19 detection using deep learning approach.

In International journal of speech technology

[This retracts the article DOI: 10.1007/s10772-021-09878-0.].

Al-Dhlan Kawther A

2022-Oct-13

General General

Comparative analysis of deep learning approaches for AgNOR-stained cytology samples interpretation

ArXiv Preprint

Cervical cancer is a public health problem, where the treatment has a better chance of success if detected early. The analysis is a manual process which is subject to a human error, so this paper provides a way to analyze argyrophilic nucleolar organizer regions (AgNOR) stained slide using deep learning approaches. Also, this paper compares models for instance and semantic detection approaches. Our results show that the semantic segmentation using U-Net with ResNet-18 or ResNet-34 as the backbone have similar results, and the best model shows an IoU for nucleus, cluster, and satellites of 0.83, 0.92, and 0.99 respectively. For instance segmentation, the Mask R-CNN using ResNet-50 performs better in the visual inspection and has a 0.61 of the IoU metric. We conclude that the instance segmentation and semantic segmentation models can be used in combination to make a cascade model able to select a nucleus and subsequently segment the nucleus and its respective nucleolar organizer regions (NORs).

João Gustavo Atkinson Amorim, André Victória Matias, Allan Cerentini, Luiz Antonio Buschetto Macarini, Alexandre Sherlley Onofre, Fabiana Botelho Onofre, Aldo von Wangenheim

2022-10-19

General General

Profitability of Ichimoku-Based Trading Rule in Vietnam Stock Market in the Context of the COVID-19 Outbreak.

In Computational economics

Ichimoku Kinkohyo or Ichimoku Cloud Chart is one of the most popular technical indicators used by traders all over the world. However, its profitability is heavily influenced by the market environment, to which it is applied. Furthermore, the COVID-19 outbreak may have an impact on the market environment as well as the performance of all technical indicators. This study is the first to look into the profitability of Ichimoku-based trading rules in the Vietnamese stock market in the context of the COVID-19 outbreak. More particularly, the COVID-19 outbreak has a positive influence on the performance of this strategy when considering the entire market as well as a variety of industries including real estate industry, food and beverage industry, resource industry, and automotive and electronic components industry. Compared to the pre-pandemic period, the return on investment obtained per each transaction using the Ichimoku-based strategy increased by roughly 8 - 9 % in the pandemic period. Compared to the Buy-and-hold method, the Ichimoku-based strategy could slightly increase Accumulated return while posing a lower risk. The findings indicate that the Ichimoku-based strategy is applicable to the Vietnam stock market, regardless of the adverse effects of the pandemic on the industries.

Che-Ngoc Ha, Do-Thi Nga, Nguyen-Trang Thao

2022-Oct-13

COVID-19, Ichimoku cloud, Non-parametric statistics, Return on Investment, Vietnamese stock market

General General

Self-learning locally-optimal hypertuning using maximum entropy, and comparison of machine learning approaches for estimating fatigue life in composite materials

ArXiv Preprint

Applications of Structural Health Monitoring (SHM) combined with Machine Learning (ML) techniques enhance real-time performance tracking and increase structural integrity awareness of civil, aerospace and automotive infrastructures. This SHM-ML synergy has gained popularity in the last years thanks to the anticipation of maintenance provided by arising ML algorithms and their ability of handling large quantities of data and considering their influence in the problem. In this paper we develop a novel ML nearest-neighbors-alike algorithm based on the principle of maximum entropy to predict fatigue damage (Palmgren-Miner index) in composite materials by processing the signals of Lamb Waves -- a non-destructive SHM technique -- with other meaningful features such as layup parameters and stiffness matrices calculated from the Classical Laminate Theory (CLT). The full data analysis cycle is applied to a dataset of delamination experiments in composites. The predictions achieve a good level of accuracy, similar to other ML algorithms, e.g. Neural Networks or Gradient-Boosted Trees, and computation times are of the same order of magnitude. The key advantages of our proposal are: (1) The automatic determination of all the parameters involved in the prediction, so no hyperparameters have to be set beforehand, which saves time devoted to hypertuning the model and also represents an advantage for autonomous, self-supervised SHM. (2) No training is required, which, in an \textit{online learning} context where streams of data are fed continuously to the model, avoids repeated training -- essential for reliable real-time, continuous monitoring.

Ismael Ben-Yelun, Miguel Diaz-Lago, Luis Saucedo-Mora, Miguel Angel Sanz, Ricardo Callado, Francisco Javier Montans

2022-10-19

General General

Review Learning: Alleviating Catastrophic Forgetting with Generative Replay without Generator

ArXiv Preprint

When a deep learning model is sequentially trained on different datasets, it forgets the knowledge acquired from previous data, a phenomenon known as catastrophic forgetting. It deteriorates performance of the deep learning model on diverse datasets, which is critical in privacy-preserving deep learning (PPDL) applications based on transfer learning (TL). To overcome this, we propose review learning (RL), a generative-replay-based continual learning technique that does not require a separate generator. Data samples are generated from the memory stored within the synaptic weights of the deep learning model which are used to review knowledge acquired from previous datasets. The performance of RL was validated through PPDL experiments. Simulations and real-world medical multi-institutional experiments were conducted using three types of binary classification electronic health record data. In the real-world experiments, the global area under the receiver operating curve was 0.710 for RL and 0.655 for TL. Thus, RL was highly effective in retaining previously learned knowledge.

Jaesung Yoo, Sunghyuk Choi, Ye Seul Yang, Suhyeon Kim, Jieun Choi, Dongkyeong Lim, Yaeji Lim, Hyung Joon Joo, Dae Jung Kim, Rae Woong Park, Hyeong-Jin Yoon, Kwangsoo Kim

2022-10-17

Public Health Public Health

Identifying pre-existing conditions and multimorbidity patterns associated with in-hospital mortality in patients with COVID-19.

In Scientific reports ; h5-index 158.0

We investigated the association between a wide range of comorbidities and COVID-19 in-hospital mortality and assessed the influence of multi morbidity on the risk of COVID-19-related death using a large, regional cohort of 6036 hospitalized patients. This retrospective cohort study was conducted using Patient Administration System Admissions and Discharges data. The International Classification of Diseases 10th edition (ICD-10) diagnosis codes were used to identify common comorbidities and the outcome measure. Individuals with lymphoma (odds ratio [OR], 2.78;95% CI,1.64-4.74), metastatic cancer (OR, 2.17; 95% CI,1.25-3.77), solid tumour without metastasis (OR, 1.67; 95% CI,1.16-2.41), liver disease (OR: 2.50, 95% CI,1.53-4.07), congestive heart failure (OR, 1.69; 95% CI,1.32-2.15), chronic obstructive pulmonary disease (OR, 1.43; 95% CI,1.18-1.72), obesity (OR, 5.28; 95% CI,2.92-9.52), renal disease (OR, 1.81; 95% CI,1.51-2.19), and dementia (OR, 1.44; 95% CI,1.17-1.76) were at increased risk of COVID-19 mortality. Asthma was associated with a lower risk of death compared to non-asthma controls (OR, 0.60; 95% CI,0.42-0.86). Individuals with two (OR, 1.79; 95% CI, 1.47-2.20; P < 0.001), and three or more comorbidities (OR, 1.80; 95% CI, 1.43-2.27; P < 0.001) were at increasingly higher risk of death when compared to those with no underlying conditions. Furthermore, multi morbidity patterns were analysed by identifying clusters of conditions in hospitalised COVID-19 patients using k-mode clustering, an unsupervised machine learning technique. Six patient clusters were identified, with recognisable co-occurrences of COVID-19 with different combinations of diseases, namely, cardiovascular (100%) and renal (15.6%) diseases in patient Cluster 1; mental and neurological disorders (100%) with metabolic and endocrine diseases (19.3%) in patient Cluster 2; respiratory (100%) and cardiovascular (15.0%) diseases in patient Cluster 3, cancer (5.9%) with genitourinary (9.0%) as well as metabolic and endocrine diseases (9.6%) in patient Cluster 4; metabolic and endocrine diseases (100%) and cardiovascular diseases (69.1%) in patient Cluster 5; mental and neurological disorders (100%) with cardiovascular diseases (100%) in patient Cluster 6. The highest mortality of 29.4% was reported in Cluster 6.

Bucholc Magda, Bradley Declan, Bennett Damien, Patterson Lynsey, Spiers Rachel, Gibson David, Van Woerden Hugo, Bjourson Anthony J

2022-Oct-15

General General

Computer-aided diagnostic for classifying chest X-ray images using deep ensemble learning.

In BMC medical imaging

BACKGROUND : Nowadays doctors and radiologists are overwhelmed with a huge amount of work. This led to the effort to design different Computer-Aided Diagnosis systems (CAD system), with the aim of accomplishing a faster and more accurate diagnosis. The current development of deep learning is a big opportunity for the development of new CADs. In this paper, we propose a novel architecture for a convolutional neural network (CNN) ensemble for classifying chest X-ray (CRX) images into four classes: viral Pneumonia, Tuberculosis, COVID-19, and Healthy. Although Computed tomography (CT) is the best way to detect and diagnoses pulmonary issues, CT is more expensive than CRX. Furthermore, CRX is commonly the first step in the diagnosis, so it's very important to be accurate in the early stages of diagnosis and treatment.

RESULTS : We applied the transfer learning technique and data augmentation to all CNNs for obtaining better performance. We have designed and evaluated two different CNN-ensembles: Stacking and Voting. This system is ready to be applied in a CAD system to automated diagnosis such a second or previous opinion before the doctors or radiology's. Our results show a great improvement, 99% accuracy of the Stacking Ensemble and 98% of accuracy for the the Voting Ensemble.

CONCLUSIONS : To minimize missclassifications, we included six different base CNN models in our architecture (VGG16, VGG19, InceptionV3, ResNet101V2, DenseNet121 and CheXnet) and it could be extended to any number as well as we expect extend the number of diseases to detected. The proposed method has been validated using a large dataset created by mixing several public datasets with different image sizes and quality. As we demonstrate in the evaluation carried out, we reach better results and generalization compared with previous works. In addition, we make a first approach to explainable deep learning with the objective of providing professionals more information that may be valuable when evaluating CRXs.

Visuña Lara, Yang Dandi, Garcia-Blas Javier, Carretero Jesus

2022-Oct-15

CNN, COVID-19 classification, Deep ensemble learning, Grad-CAM, Stacking, Voting

Public Health Public Health

Understanding and revealing the intrinsic impacts of the COVID-19 lockdown on air quality and public health in North China using machine learning.

In The Science of the total environment

To avoid the spread of COVID-19, China implemented strict prevention and control measures, resulting in dramatic variations in urban and regional air quality. With the complex effect from long-term emission mitigation and meteorology variation, an accurate evaluation of the net effect from lockdown on air quality changes has not been fully quantified. Here, we combined machine learning algorithm and Theil-Sen regression technique to eliminate meteorological and long-term trends effects on air pollutant concentrations and precisely detect concentrations changes those ascribed to lockdown measures in North China. Our results showed that, compared to the same period in 2015-2019, the adverse meteorology during the lockdown period (January 25th to March 15th) in early 2020 increased PM2.5 concentration in North China by 9.8 %, while the reduction of anthropogenic emissions led to a 32.2 % drop. Stagnant meteorological conditions have a more significant impact on the ground-level air quality in the Beijing-Tianjin-Hebei Region than that in Shanxi and Shandong provinces. After further striping out the effect of long-term emission reduction trend, the lockdown-derived NO2, PM2.5, and O3 shown variety change trend, and at -30.8 %, -27.6 %, and +10.0 %, respectively. Air pollutant changes during the lockdown could be overestimated up to 2-fold without accounting for the influences of meteorology and long-term trends. Further, with pollution reduction during the lockdown period, it would avoid 15,807 premature deaths in 40 cities. If with no deteriorate meteorological condition, the total avoided premature should increase by 1146.

Lv Yunqian, Tian Hezhong, Luo Lining, Liu Shuhan, Bai Xiaoxuan, Zhao Hongyan, Zhang Kai, Lin Shumin, Zhao Shuang, Guo Zhihui, Xiao Yifei, Yang Junqi

2022-Oct-10

Air quality, COVID-19, Disease burden, Long-term trends, Meteorological parameters, Random forest

General General

Design and development of hybrid optimization enabled deep learning model for COVID-19 detection with comparative analysis with DCNN, BIAT-GRU, XGBoost.

In Computers in biology and medicine

The recent investigation has started for evaluating the human respiratory sounds, like voice recorded, cough, and breathing from hospital confirmed Covid-19 tools, which differs from healthy person's sound. The cough-based detection of Covid-19 also considered with non-respiratory and respiratory sounds data related with all declared situations. Covid-19 is respiratory disease, which is usually produced by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). However, it is more indispensable to detect the positive cases for reducing further spread of virus, and former treatment of affected patients. With constant rise in the COVID-19 cases, there has been a constant rise in the need of efficient and safe ways to detect an infected individual. With the cases multiplying constantly, the current detecting devices like RT-PCR and fast testing kits have become short in supply. An effectual Covid-19 detection model using devised hybrid Honey Badger Optimization-based Deep Neuro Fuzzy Network (HBO-DNFN) is developed in this paper. Here, the audio signal is considered as input for detecting Covid-19. The gaussian filter is applied to input signal for removing the noises and then feature extraction is performed. The substantial features, like spectral roll-off, spectral bandwidth, Mel frequency cepstral coefficients (MFCC), spectral flatness, zero crossing rate, spectral centroid, mean square energy and spectral contract are extracted for further processing. Finally, DNFN is applied for detecting Covid-19 and the deep leaning model is trained by designed hybrid HBO algorithm. Accordingly, the developed Hybrid HBO method is newly designed by incorporating Honey Badger optimization Algorithm (HBA) and Jaya algorithm. The performance of developed Covid-19 detection model is evaluated using three metrics, like testing accuracy, sensitivity and specificity. The developed Hybrid HBO-based DNFN is outpaced than other existing approaches in terms of testing accuracy, sensitivity and specificity of "0.9176, 0.9218 and 0. 9219". All the test results are validated with the k-fold cross validation method in order to make an assessment of the generalizability of these results. When k-fold value is 9, sensitivity of existing techniques and developed JHBO-based DNFN is 0.8982, 0.8816, 0.8938, and 0.9207. The sensitivity of developed approach is improved by means of gaussian filtering model. The specificity of DCNN is 0.9125, BI-AT-GRU is 0.8926, and XGBoost is 0.9014, while developed JHBO-based DNFN is 0.9219 in k-fold value 9.

Dar Jawad Ahmad, Srivastava Kamal Kr, Ahmed Lone Sajaad

2022-Oct-03

(SARS-CoV-2) Covid-19 detection, Fuzzy, Hybrid optimization, Mel frequency cepstral coefficients, Neural network, Spectral centroid, Spectral flatness

General General

Explanatory classification of CXR images into COVID-19, Pneumonia and Tuberculosis using deep learning and XAI.

In Computers in biology and medicine

Chest X-ray (CXR) images are considered useful to monitor and investigate a variety of pulmonary disorders such as COVID-19, Pneumonia, and Tuberculosis (TB). With recent technological advancements, such diseases may now be recognized more precisely using computer-assisted diagnostics. Without compromising the classification accuracy and better feature extraction, deep learning (DL) model to predict four different categories is proposed in this study. The proposed model is validated with publicly available datasets of 7132 chest x-ray (CXR) images. Furthermore, results are interpreted and explained using Gradient-weighted Class Activation Mapping (Grad-CAM), Local Interpretable Modelagnostic Explanation (LIME), and SHapley Additive exPlanation (SHAP) for better understandably. Initially, convolution features are extracted to collect high-level object-based information. Next, shapely values from SHAP, predictability results from LIME, and heatmap from Grad-CAM are used to explore the black-box approach of the DL model, achieving average test accuracy of 94.31 ± 1.01% and validation accuracy of 94.54 ± 1.33 for 10-fold cross validation. Finally, in order to validate the model and qualify medical risk, medical sensations of classification are taken to consolidate the explanations generated from the eXplainable Artificial Intelligence (XAI) framework. The results suggest that XAI and DL models give clinicians/medical professionals persuasive and coherent conclusions related to the detection and categorization of COVID-19, Pneumonia, and TB.

Bhandari Mohan, Shahi Tej Bahadur, Siku Birat, Neupane Arjun

2022-Oct-03

COVID-19, Deep learning, Grad-CAM, LIME, Pneumonia, SHAP, Tuberculosis, eXplainable AI

Radiology Radiology

The natural language processing of radiology requests and reports of chest imaging: Comparing five transformer models' multilabel classification and a proof-of-concept study.

In Health informatics journal ; h5-index 25.0

BACKGROUND : Radiology requests and reports contain valuable information about diagnostic findings and indications, and transformer-based language models are promising for more accurate text classification.

METHODS : In a retrospective study, 2256 radiologist-annotated radiology requests (8 classes) and reports (10 classes) were divided into training and testing datasets (90% and 10%, respectively) and used to train 32 models. Performance metrics were compared by model type (LSTM, Bertje, RobBERT, BERT-clinical, BERT-multilingual, BERT-base), text length, data prevalence, and training strategy. The best models were used to predict the remaining 40,873 cases' categories of the datasets of requests and reports.

RESULTS : The RobBERT model performed the best after 4000 training iterations, resulting in AUC values ranging from 0.808 [95% CI (0.757-0.859)] to 0.976 [95% CI (0.956-0.996)] for the requests and 0.746 [95% CI (0.689-0.802)] to 1.0 [95% CI (1.0-1.0)] for the reports. The AUC for the classification of normal reports was 0.95 [95% CI (0.922-0.979)]. The predicted data demonstrated variability of both diagnostic yield for various request classes and request patterns related to COVID-19 hospital admission data.

CONCLUSION : Transformer-based natural language processing is feasible for the multilabel classification of chest imaging request and report items. Diagnostic yield varies with the information in the requests.

Olthof Allard W, van Ooijen Peter Ma, Cornelissen Ludo J

chest imaging, data mining, machine learning, natural language processing, radiology

General General

Smart healthcare: A prospective future medical approach for COVID-19.

In Journal of the Chinese Medical Association : JCMA

COVID-19 has greatly affected human life for over 3 years. In this review, we focus on smart healthcare solutions that address major requirements for coping with the COVID-19 pandemic, including (1) the continuous monitoring of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), (2) patient stratification with distinct short-term outcomes (e.g. mild or severe diseases) and long-term outcomes (e.g. long COVID), and (3) adherence to medication and treatments for patients with COVID-19. Smart healthcare often utilizes medical artificial intelligence (AI) and cloud computing and integrates cutting-edge biological and optoelectronic techniques. These are valuable technologies for addressing the unmet needs in the management of COVID. By leveraging deep/machine learning (DL/ML) capabilities and big data, medical AI can perform precise prognosis predictions and provide reliable suggestions for physicians' decision-making. Through the assistance of the Internet of Medical Things (IoMT), which encompasses wearable devices, smartphone apps, Internet-based drug delivery systems, and telemedicine technologies, the status of mild cases can be continuously monitored and medications provided at home without the need for hospital care. In cases that develop into severe cases, emergency feedback can be provided through the hospital for rapid treatment. Smart healthcare can possibly prevent the development of severe COVID-19 cases and therefore lower the burden on intensive care units.

Yang De-Ming, Chang Tai-Jay, Hung Kai-Feng, Wang Mong-Lien, Cheng Yen-Fu, Chiang Su-Hua, Chen Mei-Fang, Liao Yi-Ting, Lai Wei-Qun, Liang Kung-Hao

2022-Oct-12

Public Health Public Health

Discussions About COVID-19 Vaccination on Twitter in Turkey: Sentiment Analysis.

In Disaster medicine and public health preparedness

OBJECTIVES : The present study aims to examine COVID-19 vaccination discussions on Twitter in Turkey and conduct sentiment analysis.

METHODS : The current study performed sentiment analysis of Twitter data with artificial intelligence (AI)'s Natural Language Processing (NLP) method. The tweets were retrieved retrospectively from March 10, 2020, when the first Covid-19 case was seen in Turkey, to April 18, 2022. 10308 tweets accessed. The data were filtered before analysis due to excessive noise. First, the text is tokenized. Many steps were applied in normalizing texts. Tweets about the COVID-19 vaccines were classified according to basic emotion categories using sentiment analysis. The resulting dataset was used for training and testing machine learning classifiers.

RESULTS : It was determined that 7.50% of the tweeters had positive, 0.59% negative, and 91.91% neutral opinions about the COVID-19 vaccination. When the accuracy values of the ML algorithms used in this study were examined, it was seen that the XGB algorithm had higher scores.

CONCLUSIONS : Three out of four tweets consist of negative and neutral emotions. The responsibility of professional chambers and the public is essential in transforming these neutral and negative feelings into positive ones.

Özsezer Gözde, Mermer Gülengül

2022-Oct-13

COVID-19, Twitter, Vaccine, sentiment analysis

Public Health Public Health

Deep learning techniques for detecting and recognizing face masks: A survey.

In Frontiers in public health

The year 2020 brought many changes to the lives of people all over the world with the outbreak of COVID-19; we saw lockdowns for months and deaths of many individuals, which set the world economy back miles. As research was conducted to create vaccines and cures that would eradicate the virus, precautionary measures were imposed on people to help reduce the spread the disease. These measures included washing of hands, appropriate distancing in social gatherings and wearing of masks to cover the face and nose. But due to human error, most people failed to adhere to this face mask rule and this could be monitored using artificial intelligence. In this work, we carried out a survey on Masked Face Recognition (MFR) and Occluded Face Recognition (OFR) deep learning techniques used to detect whether a face mask was being worn. The major problem faced by these models is that people often wear face masks incorrectly, either not covering the nose or mouth, which is equivalent to not wearing it at all. The deep learning algorithms detected the covered features on the face to ensure that the correct parts of the face were covered and had amazingly effective results.

Alturki Rahaf, Alharbi Maali, AlAnzi Ftoon, Albahli Saleh

2022

convolutional neural network, crowd monitoring, face mask, public health, transfer learning

General General

Warfarin anticoagulation management during the COVID-19 pandemic: The role of internet clinic and machine learning.

In Frontiers in pharmacology

Background: Patients who received warfarin require constant monitoring by hospital staff. However, social distancing and stay-at-home orders, which were universally adopted strategies to avoid the spread of COVID-19, led to unprecedented challenges. This study aimed to optimize warfarin treatment during the COVID-19 pandemic by determining the role of the Internet clinic and developing a machine learning (ML) model to predict anticoagulation quality. Methods: This retrospective study enrolled patients who received warfarin treatment in the hospital anticoagulation clinic (HAC) and "Internet + Anticoagulation clinic" (IAC) of the Nanjing Drum Tower Hospital between January 2020 and September 2021. The primary outcome was the anticoagulation quality of patients, which was evaluated by both the time in therapeutic range (TTR) and international normalized ratio (INR) variability. Anticoagulation quality and incidence of adverse events were compared between HAC and IAC. Furthermore, five ML algorithms were used to develop the anticoagulation quality prediction model, and the SHAP method was introduced to rank the feature importance. Results: Totally, 241 patients were included, comprising 145 patients in the HAC group and 96 patients in the IAC group. In the HAC group and IAC group, 73.1 and 69.8% (p = 0.576) of patients achieved good anticoagulation quality, with the average TTR being 79.9 ± 20.0% and 80.6 ± 21.1%, respectively. There was no significant difference in the incidence of adverse events between the two groups. Evaluating the five ML models using the test set, the accuracy of the XGBoost model was 0.767, and the area under the receiver operating characteristic curve was 0.808, which showed the best performance. The results of the SHAP method revealed that age, education, hypertension, aspirin, and amiodarone were the top five important features associated with poor anticoagulation quality. Conclusion: The IAC contributed to a novel management method for patients who received warfarin during the COVID-19 pandemic, as effective as HAC and with a low risk of virus transmission. The XGBoost model could accurately select patients at a high risk of poor anticoagulation quality, who could benefit from active intervention.

Dai Meng-Fei, Li Shu-Yue, Zhang Ji-Fan, Wang Bao-Yan, Zhou Lin, Yu Feng, Xu Hang, Ge Wei-Hong

2022

COVID-19, anticoagulation quality, internet, machine learning, telemedicine, warfarin

General General

Reduced B cell antigenicity of Omicron lowers host serologic response.

In Cell reports ; h5-index 119.0

The SARS-CoV-2 Omicron variant evades most neutralizing vaccine-induced antibodies and is associated with lower antibody titers upon breakthrough infections than previous variants. However, the mechanism remains unclear. Here, we find using a geometric deep-learning model that Omicron's extensively mutated receptor binding site (RBS) features reduced antigenicity compared with previous variants. Mice immunization experiments with different recombinant receptor binding domain (RBD) variants confirm that the serological response to Omicron is drastically attenuated and less potent. Analyses of serum cross-reactivity and competitive ELISA reveal a reduction in antibody response across both variable and conserved RBD epitopes. Computational modeling confirms that the RBS has a potential for further antigenicity reduction while retaining efficient receptor binding. Finally, we find a similar trend of antigenicity reduction over decades for hCoV229E, a common cold coronavirus. Thus, our study explains the reduced antibody titers associated with Omicron infection and reveals a possible trajectory of future viral evolution.

Tubiana Jérôme, Xiang Yufei, Fan Li, Wolfson Haim J, Chen Kong, Schneidman-Duhovny Dina, Shi Yi

2022-Sep-28

CP: Immunology, CP: Microbiology, Omicron variant of concern, SARS-CoV-2, antigenicity, computational structural biology, deep learning, spike protein

General General

A review about COVID-19 in the MENA region: environmental concerns and machine learning applications.

In Environmental science and pollution research international

Coronavirus disease 2019 (COVID-19) has delayed global economic growth, which has affected the economic life globally. On the one hand, numerous elements in the environment impact the transmission of this new coronavirus. Every country in the Middle East and North Africa (MENA) area has a different population density, air quality and contaminants, and water- and land-related conditions, all of which influence coronavirus transmission. The World Health Organization (WHO) has advocated fast evaluations to guide policymakers with timely evidence to respond to the situation. This review makes four unique contributions. One, many data about the transmission of the new coronavirus in various sorts of settings to provide clear answers to the current dispute over the virus's transmission were reviewed. Two, highlight the most significant application of machine learning to forecast and diagnose severe acute respiratory syndrome coronavirus (SARS-CoV-2). Three, our insights provide timely and accurate information along with compelling suggestions and methodical directions for investigators. Four, the present study provides decision-makers and community leaders with information on the effectiveness of environmental controls for COVID-19 dissemination.

Meskher Hicham, Belhaouari Samir Brahim, Thakur Amrit Kumar, Sathyamurthy Ravishankar, Singh Punit, Khelfaoui Issam, Saidur Rahman

2022-Oct-12

Artificial intelligent, COVID-19, Environmental analysis, MENA, Machine learning, Meteorological factors

General General

Angiotensin-converting Enzyme-2 (ACE2) Expression in Pediatric Liver Disease.

In Applied immunohistochemistry & molecular morphology : AIMM

The membrane protein angiotensin-converting enzyme-2 (ACE2) has gained notoriety as the receptor for severe acute respiratory syndrome coronavirus 2. Prior evidence has shown ACE2 is expressed within the liver but its function has not been fully discerned. Here, we utilized novel methodology to assess ACE2 expression in pediatric immune-mediated liver disease to better understand its presence in liver diseases and its role during infections such as COVID-19. We stained liver tissue with ACE2-specific immunofluorescent antibodies, analyzed via confocal microscopy. Computational deep learning-based segmentation models identified nuclei and cells, allowing the quantification of mean cellular and cytosolic immunofluorescent. Spatial transcriptomics provided high-throughput gene expression analysis in tissue to determine cellular composition for ACE2 expression. ACE2 plasma expression was quantified via enzyme-linked immunosorbent assay. High ACE2 expression was seen at the apical surface of cholangiocytes, with lower expression within hepatocyte cytosol and nonparenchymal cells (P<0.001). Children with liver disease had higher ACE2 hepatic expression than pediatric control tissue (P<0.001). Adult control tissue had higher expression than pediatric control (P<0.001). Plasma ACE2 was not found to be statistically different between samples. Spatial transcriptomics identified cell composition of ACE2-expressing spots containing antibody-secreting cells. Our results show ACE2 expression throughout the liver, with strongest localization to cholangiocyte membranes. Machine learning can be used to rapidly identify hepatic cellular components for histologic analysis. ACE2 expression in the liver may be increased in pediatric liver disease. Future work is needed to better understand the role of ACE2 in chronic disease and acute infections.

Stevens James P, Kolachala Vasantha L, Joshi Gaurav N, Nagpal Sini, Gibson Greg, Gupta Nitika A

2022-Oct-11

General General

Variation in the ACE2 receptor has limited utility for SARS-CoV-2 host prediction

bioRxiv Preprint

Transmission of SARS-CoV-2 from humans to other species threatens wildlife conservation and may create novel sources of viral diversity for future zoonotic transmission. A variety of computational heuristics have been developed to pre-emptively identify susceptible host species based on variation in the ACE2 receptor used for viral entry. However, the predictive performance of these heuristics remains unknown. Using a newly-compiled database of 96 species we show that, while variation in ACE2 can be used by machine learning models to accurately predict animal susceptibility to sarbecoviruses (accuracy = 80.2%, binomial confidence interval [CI]: 70.8 - 87.6%), the sites informing predictions have no known involvement in virus binding and instead recapitulate host phylogeny. Models trained on host phylogeny alone performed equally well (accuracy = 84.4%, CI: 75.5 - 91.0%) and at a level equivalent to retrospective assessments of accuracy for previously published models. These results suggest that the predictive power of ACE2-based models derives from strong correlations with host phylogeny rather than processes which can be mechanistically linked to infection biology. Further, biased availability of ACE2 sequences misleads projections of the number and geographic distribution of at-risk species. Models based on host phylogeny reduce this bias, but identify a very large number of susceptible species, implying that model predictions must be combined with local knowledge of exposure risk to practically guide surveillance. Identifying barriers to viral infection or onward transmission beyond receptor binding and incorporating data which are independent of host phylogeny will be necessary to manage the ongoing risk of establishment of novel animal reservoirs of SARS-CoV-2.

Mollentze, N.; Keen, D.; Munkhbayar, U.; Biek, R.; Streicker, D. G.

2022-10-13

General General

A Large-Scale Annotated Multivariate Time Series Aviation Maintenance Dataset from the NGAFID

ArXiv Preprint

This paper presents the largest publicly available, non-simulated, fleet-wide aircraft flight recording and maintenance log data for use in predicting part failure and maintenance need. We present 31,177 hours of flight data across 28,935 flights, which occur relative to 2,111 unplanned maintenance events clustered into 36 types of maintenance issues. Flights are annotated as before or after maintenance, with some flights occurring on the day of maintenance. Collecting data to evaluate predictive maintenance systems is challenging because it is difficult, dangerous, and unethical to generate data from compromised aircraft. To overcome this, we use the National General Aviation Flight Information Database (NGAFID), which contains flights recorded during regular operation of aircraft, and maintenance logs to construct a part failure dataset. We use a novel framing of Remaining Useful Life (RUL) prediction and consider the probability that the RUL of a part is greater than 2 days. Unlike previous datasets generated with simulations or in laboratory settings, the NGAFID Aviation Maintenance Dataset contains real flight records and maintenance logs from different seasons, weather conditions, pilots, and flight patterns. Additionally, we provide Python code to easily download the dataset and a Colab environment to reproduce our benchmarks on three different models. Our dataset presents a difficult challenge for machine learning researchers and a valuable opportunity to test and develop prognostic health management methods

Hong Yang, Travis Desell

2022-10-13

General General

UncertaintyFuseNet: Robust uncertainty-aware hierarchical feature fusion model with Ensemble Monte Carlo Dropout for COVID-19 detection.

In An international journal on information fusion

The COVID-19 (Coronavirus disease 2019) pandemic has become a major global threat to human health and well-being. Thus, the development of computer-aided detection (CAD) systems that are capable of accurately distinguishing COVID-19 from other diseases using chest computed tomography (CT) and X-ray data is of immediate priority. Such automatic systems are usually based on traditional machine learning or deep learning methods. Differently from most of the existing studies, which used either CT scan or X-ray images in COVID-19-case classification, we present a new, simple but efficient deep learning feature fusion model, called  U n c e r t a i n t y F u s e N e t , which is able to classify accurately large datasets of both of these types of images. We argue that the uncertainty of the model's predictions should be taken into account in the learning process, even though most of the existing studies have overlooked it. We quantify the prediction uncertainty in our feature fusion model using effective Ensemble Monte Carlo Dropout (EMCD) technique. A comprehensive simulation study has been conducted to compare the results of our new model to the existing approaches, evaluating the performance of competing models in terms of Precision, Recall, F-Measure, Accuracy and ROC curves. The obtained results prove the efficiency of our model which provided the prediction accuracy of 99.08% and 96.35% for the considered CT scan and X-ray datasets, respectively. Moreover, our  U n c e r t a i n t y F u s e N e t model was generally robust to noise and performed well with previously unseen data. The source code of our implementation is freely available at: https://github.com/moloud1987/UncertaintyFuseNet-for-COVID-19-Classification.

Abdar Moloud, Salari Soorena, Qahremani Sina, Lam Hak-Keung, Karray Fakhri, Hussain Sadiq, Khosravi Abbas, Acharya U Rajendra, Makarenkov Vladimir, Nahavandi Saeid

2022-Oct-05

COVID-19, Deep learning, Early fusion, Feature fusion, Uncertainty quantification

General General

Post-script-Retail forecasting: Research and practice.

In International journal of forecasting

This note updates the 2019 review article "Retail forecasting: Research and practice" in the context of the COVID-19 pandemic and the substantial new research on machine-learning algorithms, when applied to retail. It offers new conclusions and challenges for both research and practice in retail demand forecasting.

Fildes Robert, Kolassa Stephan, Ma Shaohui

COVID-19, Disruption, Instability, Machine learning, Omni-retailing, Online retail, Structural change

General General

Machine learning and automatic ARIMA/Prophet models-based forecasting of COVID-19: methodology, evaluation, and case study in SAARC countries.

In Stochastic environmental research and risk assessment : research journal

Machine learning (ML) has proved to be a prominent study field while solving complex real-world problems. The whole globe has suffered and continues suffering from Coronavirus disease 2019 (COVID-19), and its projections need to be forecasted. In this article, we propose and derive an autoregressive modeling framework based on ML and statistical methods to predict confirmed cases of COVID-19 in the South Asian Association for Regional Cooperation (SAARC) countries. Automatic forecasting models based on autoregressive integrated moving average (ARIMA) and Prophet time series structures, as well as extreme gradient boosting, generalized linear model elastic net (GLMNet), and random forest ML techniques, are introduced and applied to COVID-19 data from the SAARC countries. Different forecasting models are compared by means of selection criteria. By using evaluation metrics, the best and suitable models are selected. Results prove that the ARIMA model is found to be suitable and ideal for forecasting confirmed infected cases of COVID-19 in these countries. For the confirmed cases in Afghanistan, Bangladesh, India, Maldives, and Sri Lanka, the ARIMA model is superior to the other models. In Bhutan, the Prophet time series model is appropriate for predicting such cases. The GLMNet model is more accurate than other time-series models for Nepal and Pakistan. The random forest model is excluded from forecasting because of its poor fit.

Sardar Iqra, Akbar Muhammad Azeem, Leiva Víctor, Alsanad Ahmed, Mishra Pradeep

2022-Oct-05

Artificial intelligence, Facebook Prophet algorithm, GLM, R software, SARS-CoV-2, South Asian Association for Regional Cooperation countries, Time-series models

General General

Predicting pattern of coronavirus using X-ray and CT scan images.

In Network modeling and analysis in health informatics and bioinformatics

Novel coronavirus is a disease that can propagate easily with very minute carelessness and with very little physical contact between people. Presently, the world's central health institution called the World Health Organization has approved and advised the Reverse Transcription-Polymerase Chain Reaction (RT-PCR) swab test as the most important and effective diagnostic method to confirm if a patient has COVID-19 symptoms or not. This test takes at least a day for revealing the results, depending on the feasible resources in the neighborhood. Moreover, the RT-PCR test gives sometimes false positive results and slow in the process. To keep the potential virus carriers and potential causes of the disease quarantined as early as possible, there is still a requirement for a much faster and more accurate diagnostic process to supplement RT-PCR test of finding the patients affected by the virus. In this regard, radiological images such as X-ray and CT (Computerized Tomography) scan are found to be useful. The X-ray and CT scan have good screening modality; they are quick at capturing and finding and widely available around the world. Therefore, a deep learning model, which makes use of CT scan and X-ray images, has been proposed to automate and analyze the diagnostic process by utilizing Convolutional Neural Network (CNN). This model makes use of InceptionV3 deep learning model, a type of CNN. It is a lightweight deep learning model that is apt for mobile, laptop, and tablet platforms. The proposed model requires low memory space and gives an accuracy of about 96%, sensitivity of 93.48% for CXRs (Chest X-rays) and accuracy of 93%, sensitivity of 89.81 % for the CT scan images respectively. The proposed model is also compared with other deep learning models like VGG 16 (Visual Geometry Group), ResNet50V2 (Residual Network) and other existing deep learning models and it is found to be better in terms of accuracy and other performance parameters. Further, a web application has been developed from the proposed model. The web application is able to detect COVID-19 cases from the CT scan and X-ray images with significant accuracy.

Khurana Batra Payal, Aggarwal Paras, Wadhwa Dheeraj, Gulati Mehul

2022

CT scan, Convolutional Neural Network (CNN), Coronavirus, Deep learning, Prediction, X-ray

General General

ADL-CDF: A Deep Learning Framework for COVID-19 Detection from CT Scans Towards an Automated Clinical Decision Support System.

In Arabian journal for science and engineering

The emergence of deep learning has paved to solve many problems in the real world. COVID-19 pandemic, since the late 2019, has been affecting lives of people across the globe. Chest CT scan images are used to detect it and know its severity in patients. The problem with many existing solutions in COVID-19 detection using CT scan images is that inability to detect the infection when it is in initial stages. As the infection can exist on varied scales, there is need for more comprehensive approach that can ascertain the disease at all scales. Towards this end, we proposed a deep learning-based framework known as Automated Deep Learning-based COVID-19 Detection Framework (ADL-CDF). It does not need a human medical expert in diagnosis as it is capable of detecting automatically. The framework is assisted by two algorithms that involve image processing and deep learning. The first algorithm known as Region of Interest (ROI)-based Image Filtering (ROI-IF) which analyses given input CT scan images of a patient and discards the ones where ROI is missing. This algorithm minimizes time taken for processing besides reducing false positive rate. The second algorithm is known as Multi-Scale Feature Selection algorithm that fits into the deep learning framework's pipeline to leverage detection performance of the ADL-CDF. The proposed framework is evaluated against ResNet50V2 and Xception. Our empirical study revealed that our model outperforms the state of the art.

Saheb Shaik Khasim, Narayanan B, Rao Thota Venkat Narayana

2022-Oct-04

Convolutional neural networks, Covid-19, Deep learning, Medical image analysis, Multi-scale feature selection

Public Health Public Health

Detecting COVID-19 infection status from chest X-ray and CT scan via single transfer learning-driven approach.

In Frontiers in genetics ; h5-index 62.0

COVID-19 has caused over 528 million infected cases and over 6.25 million deaths since its outbreak in 2019. The uncontrolled transmission of the SARS-CoV-2 virus has caused human suffering and the death of uncountable people. Despite the continuous effort by the researchers and laboratories, it has been difficult to develop reliable efficient and stable vaccines to fight against the rapidly evolving virus strains. Therefore, effectively preventing the transmission in the community and globally has remained an urgent task since its outbreak. To avoid the rapid spread of infection, we first need to identify the infected individuals and isolate them. Therefore, screening computed tomography (CT scan) and X-ray can better separate the COVID-19 infected patients from others. However, one of the main challenges is to accurately identify infection from a medical image. Even experienced radiologists often have failed to do it accurately. On the other hand, deep learning algorithms can tackle this task much easier, faster, and more accurately. In this research, we adopt the transfer learning method to identify the COVID-19 patients from normal individuals when there is an inadequacy of medical image data to save time by generating reliable results promptly. Furthermore, our model can perform both X-rays and CT scan. The experimental results found that the introduced model can achieve 99.59% accuracy for X-rays and 99.95% for CT scan images. In summary, the proposed method can effectively identify COVID-19 infected patients, could be a great way which will help to classify COVID-19 patients quickly and prevent the viral transmission in the community.

Ghose Partho, Alavi Muhaddid, Tabassum Mehnaz, Ashraf Uddin Md, Biswas Milon, Mahbub Kawsher, Gaur Loveleen, Mallik Saurav, Zhao Zhongming

2022

COVID-19, CT scan, classification, deep learning, transfer learning, x-ray

General General

Twitter data sentiment analysis of tourism in Thailand during the COVID-19 pandemic using machine learning.

In Heliyon

The coronavirus disease 2019 (COVID-19) pandemic has severely affected Thailand's economy, which relies heavily on tourism. In this study, we labeled the sentiment and intention classes of English-language tweets related to tourism in Bangkok, Chiang Mai, and Phuket. Then, the accuracy of three machine learning algorithms (decision tree, random forest, and support vector machine) in predicting the sentiments and intentions of the tweets was investigated. The support vector machine algorithm provided the best results for sentiment analysis, with a maximum accuracy of 77.4%. In the intention analysis, the random forest algorithm achieved an accuracy of 95.4%. In a subsequent preliminary qualitative content analysis, the top 10 words found in each sentiment and intention class were gathered to provide insights and suggestions to help increase tourism in Thailand. The results of this study suggest that to help restore tourism in Thailand, tourist destinations, natural attractions, restaurants, and nightlife should be promoted. In addition, the two main concerns of tourists to Thailand should be addressed: COVID-19 and current political tensions.

Leelawat Natt, Jariyapongpaiboon Sirawit, Promjun Arnon, Boonyarak Samit, Saengtabtim Kumpol, Laosunthara Ampan, Yudha Alfan Kurnia, Tang Jing

2022-Oct

COVID19, Machine learning, Sentiment analysis, Thailand, Tourism, Tweet

General General

The Commoditization of AI for Molecule Design.

In Artificial intelligence in the life sciences

Anyone involved in designing or finding molecules in the life sciences over the past few years has witnessed a dramatic change in how we now work due to the COVID-19 pandemic. Computational technologies like artificial intelligence (AI) seemed to become ubiquitous in 2020 and have been increasingly applied as scientists worked from home and were separated from the laboratory and their colleagues. This shift may be more permanent as the future of molecule design across different industries will increasingly require machine learning models for design and optimization of molecules as they become "designed by AI". AI and machine learning has essentially become a commodity within the pharmaceutical industry. This perspective will briefly describe our personal opinions of how machine learning has evolved and is being applied to model different molecule properties that crosses industries in their utility and ultimately suggests the potential for tight integration of AI into equipment and automated experimental pipelines. It will also describe how many groups have implemented generative models covering different architectures, for de novo design of molecules. We also highlight some of the companies at the forefront of using AI to demonstrate how machine learning has impacted and influenced our work. Finally, we will peer into the future and suggest some of the areas that represent the most interesting technologies that may shape the future of molecule design, highlighting how we can help increase the efficiency of the design-make-test cycle which is currently a major focus across industries.

Urbina Fabio, Ekins Sean

2022-Dec

Artificial intelligence, design-make-test, machine learning, molecule design, recurrent neural networks

Radiology Radiology

Development and validation of chest CT-based imaging biomarkers for early stage COVID-19 screening.

In Frontiers in public health

Coronavirus Disease 2019 (COVID-19) is currently a global pandemic, and early screening is one of the key factors for COVID-19 control and treatment. Here, we developed and validated chest CT-based imaging biomarkers for COVID-19 patient screening from two independent hospitals with 419 patients. We identified the vasculature-like signals from CT images and found that, compared to healthy and community acquired pneumonia (CAP) patients, COVID-19 patients display a significantly higher abundance of these signals. Furthermore, unsupervised feature learning led to the discovery of clinical-relevant imaging biomarkers from the vasculature-like signals for accurate and sensitive COVID-19 screening that have been double-blindly validated in an independent hospital (sensitivity: 0.941, specificity: 0.920, AUC: 0.971, accuracy 0.931, F1 score: 0.929). Our findings could open a new avenue to assist screening of COVID-19 patients.

Liu Xiao-Ping, Yang Xu, Xiong Miao, Mao Xuanyu, Jin Xiaoqing, Li Zhiqiang, Zhou Shuang, Chang Hang

2022

Coronavirus Disease 2019 (COVID-19), artificial intelligence, biomedical imaging application, chest CT image, imaging biomarker, multicentric retrospective study

General General

EffViT-COVID: A dual-path network for COVID-19 percentage estimation.

In Expert systems with applications

The first case of novel Coronavirus (COVID-19) was reported in December 2019 in Wuhan City, China and led to an international outbreak. This virus causes serious respiratory illness and affects several other organs of the body differently for different patient. Worldwide, several waves of this infection have been reported, and researchers/doctors are working hard to develop novel solutions for the COVID diagnosis. Imaging and vision-based techniques are widely explored for the prediction of COVID-19; however, COVID infection percentage estimation is under explored. In this work, we propose a novel framework for the estimation of COVID-19 infection percentage based on deep learning techniques. The proposed network utilizes the features from vision transformers and CNN (Convolutional Neural Networks), specifically EfficientNet-B7. The features of both are fused together for preparing an information-rich feature vector that contributes to a more precise estimation of infection percentage. We evaluate our model on the Per-COVID-19 dataset (Bougourzi et al., 2021b) which comprises labelled CT data of COVID-19 patients. For the evaluation of the model on this dataset, we employ the most widely-used slice-level metrics, i.e., Pearson correlation coefficient (PC), Mean absolute error (MAE), and Root mean square error (RMSE). The network outperforms the other state-of-the-art methods and achieves  0 . 9886 ± 0 . 009 ,  1 . 23 ± 0 . 378 , and  3 . 12 ± 1 . 56 , PC, MAE, and RMSE, respectively, using a 5-fold cross-validation technique. In addition, the overall average difference in the actual and predicted infection percentage is observed to be  < 2 % . In conclusion, the detailed experimental results reveal the robustness and efficiency of the proposed network.

Chauhan Joohi, Bedi Jatin

2022-Oct-03

COVID-19, Deep network, EfficientNet-B7, Huber loss, Percentage estimation, Vision transformer

General General

Predicting South Korean adolescents vulnerable to obesity after the COVID-19 pandemic using categorical boosting and shapley additive explanation values: A population-based cross-sectional survey.

In Frontiers in pediatrics

Objective : This study identified factors related to adolescent obesity during the COVID-19 pandemic by using machine learning techniques and developed a model for predicting high-risk obesity groups among South Korean adolescents based on the result.

Materials and methods : This study analyzed 50,858 subjects (male: 26,535 subjects, and female: 24,323 subjects) between 12 and 18 years old. Outcome variables were classified into two classes (normal or obesity) based on body mass index (BMI). The explanatory variables included demographic factors, mental health factors, life habit factors, exercise factors, and academic factors. This study developed a model for predicting adolescent obesity by using multiple logistic regressions that corrected all confounding factors to understand the relationship between predictors for South Korean adolescent obesity by inputting the seven variables with the highest Shapley values found in categorical boosting (CatBoost).

Results : In this study, the top seven variables with a high impact on model output (based on SHAP values in CatBoost) were gender, mean sitting hours per day, the number of days of conducting strength training in the past seven days, academic performance, the number of days of drinking soda in the past seven days, the number of days of conducting the moderate-intensity physical activity for 60 min or more per day in the past seven days, and subjective stress perception level.

Conclusion : To prevent obesity in adolescents, it is required to detect adolescents vulnerable to obesity early and conduct monitoring continuously to manage their physical health.

Byeon Haewon

2022

COVID-19 pandemic, CatBoost, adolescent, machine learning, obesity

General General

COVID-19-related Nepali Tweets Classification in a Low Resource Setting

ArXiv Preprint

Billions of people across the globe have been using social media platforms in their local languages to voice their opinions about the various topics related to the COVID-19 pandemic. Several organizations, including the World Health Organization, have developed automated social media analysis tools that classify COVID-19-related tweets into various topics. However, these tools that help combat the pandemic are limited to very few languages, making several countries unable to take their benefit. While multi-lingual or low-resource language-specific tools are being developed, they still need to expand their coverage, such as for the Nepali language. In this paper, we identify the eight most common COVID-19 discussion topics among the Twitter community using the Nepali language, set up an online platform to automatically gather Nepali tweets containing the COVID-19-related keywords, classify the tweets into the eight topics, and visualize the results across the period in a web-based dashboard. We compare the performance of two state-of-the-art multi-lingual language models for Nepali tweet classification, one generic (mBERT) and the other Nepali language family-specific model (MuRIL). Our results show that the models' relative performance depends on the data size, with MuRIL doing better for a larger dataset. The annotated data, models, and the web-based dashboard are open-sourced at https://github.com/naamiinepal/covid-tweet-classification.

Rabin Adhikari, Safal Thapaliya, Nirajan Basnet, Samip Poudel, Aman Shakya, Bishesh Khanal

2022-10-11

General General

Access to online learning: Machine learning analysis from a social justice perspective.

In Education and information technologies

Access to education is the first step to benefiting from it. Although cumulative online learning experience is linked academic learning gains, between-country inequalities mean that large populations are prevented from accumulating such experience. Low-and-middle-income countries are affected by disadvantages in infrastructure such as internet access and uncontextualised learning content, and parents who are less available and less well-resourced than in high-income countries. COVID-19 has exacerbated the global inequalities, with girls affected more than boys in these regions. Therefore, the present research mined online learning data to identify features that are important for access to online learning. Data mining of 54,842,787 initial (random subsample n = 5000) data points from one online learning platform was conducted by partnering theory with data in model development. Following examination of a theory-led machine learning model, a data-led approach was taken to reach a final model. The final model was used to derive Shapley values for feature importance. As expected, country differences, gender, and COVID-19 were important features in access to online learning. The data-led model development resulted in additional insights not examined in the initial, theory-led model: namely, the importance of Math ability, year of birth, session difficulty level, month of birth, and time taken to complete a session.

McIntyre Nora A

2022-Oct-04

COVID-19, Country inequalities, Educational access, Machine learning, Online learning

General General

Not Good Times for Lies: Misinformation Detection on the Russia-Ukraine War, COVID-19, and Refugees

ArXiv Preprint

Misinformation spread in online social networks is an urgent-to-solve problem having harmful consequences that threaten human health, public safety, economics, and so on. In this study, we construct a novel dataset, called MiDe-22, having 5,284 English and 5,064 Turkish tweets with their misinformation labels under several recent events, including the Russia-Ukraine war, COVID-19 pandemic, and Refugees. Moreover, we provide the user engagements to the tweets in terms of likes, replies, retweets, and quotes. We present a detailed data analysis with descriptive statistics and temporal analysis, and provide the experimental results of a benchmark evaluation for misinformation detection on our novel dataset.

Cagri Toraman, Oguzhan Ozcelik, Furkan Şahinuç, Fazli Can

2022-10-11

General General

Computer especially AI-assisted drug virtual screening and design in traditional Chinese medicine.

In Phytomedicine : international journal of phytotherapy and phytopharmacology

BACKGROUND : Traditional Chinese medicine (TCM), as a significant part of the global pharmaceutical science, the abundant molecular compounds it contains is a valuable potential source of designing and screening new drugs. However, due to the un-estimated quantity of the natural molecular compounds and diversity of the related problems drug discovery such as precise screening of molecular compounds or the evaluation of efficacy, physicochemical properties and pharmacokinetics, it is arduous for researchers to design or screen applicable compounds through old methods. With the rapid development of computer technology recently, especially artificial intelligence (AI), its innovation in the field of virtual screening contributes to an increasing efficiency and accuracy in the process of discovering new drugs.

PURPOSE : This study systematically reviewed the application of computational approaches and artificial intelligence in drug virtual filtering and devising of TCM and presented the potential perspective of computer-aided TCM development.

STUDY DESIGN : We made a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Then screening the most typical articles for our research.

METHODS : The systematic review was performed by following the PRISMA guidelines. The databases PubMed, EMBASE, Web of Science, CNKI were used to search for publications that focused on computer-aided drug virtual screening and design in TCM.

RESULT : Totally, 42 corresponding articles were included in literature reviewing. Aforementioned studies were of great significance to the treatment and cost control of many challenging diseases such as COVID-19, diabetes, Alzheimer's Disease (AD), etc. Computational approaches and AI were widely used in virtual screening in the process of TCM advancing, which include structure-based virtual screening (SBVS) and ligand-based virtual screening (LBVS). Besides, computational technologies were also extensively applied in absorption, distribution, metabolism, excretion and toxicity (ADMET) prediction of candidate drugs and new drug design in crucial course of drug discovery.

CONCLUSIONS : The applications of computer and AI play an important role in the drug virtual screening and design in the field of TCM, with huge application prospects.

Lin Yumeng, Zhang You, Wang Dongyang, Yang Bowen, Shen Ying-Qiang

2022-Oct-01

Artificial intelligence (AI), Computer-assisted, Drug screening, Drug design, Natural products, Traditional Chinese medicine (TCM)

General General

Novel chaotic oppositional fruit fly optimization algorithm for feature selection applied on COVID 19 patients' health prediction.

In PloS one ; h5-index 176.0

The fast-growing quantity of information hinders the process of machine learning, making it computationally costly and with substandard results. Feature selection is a pre-processing method for obtaining the optimal subset of features in a data set. Optimization algorithms struggle to decrease the dimensionality while retaining accuracy in high-dimensional data set. This article proposes a novel chaotic opposition fruit fly optimization algorithm, an improved variation of the original fruit fly algorithm, advanced and adapted for binary optimization problems. The proposed algorithm is tested on ten unconstrained benchmark functions and evaluated on twenty-one standard datasets taken from the Univesity of California, Irvine repository and Arizona State University. Further, the presented algorithm is assessed on a coronavirus disease dataset, as well. The proposed method is then compared with several well-known feature selection algorithms on the same datasets. The results prove that the presented algorithm predominantly outperform other algorithms in selecting the most relevant features by decreasing the number of utilized features and improving classification accuracy.

Bacanin Nebojsa, Budimirovic Nebojsa, K Venkatachalam, Strumberger Ivana, Alrasheedi Adel Fahad, Abouhawwash Mohamed

2022

Radiology Radiology

Evaluation of Federated Learning Variations for COVID-19 diagnosis using Chest Radiographs from 42 US and European hospitals.

In Journal of the American Medical Informatics Association : JAMIA

OBJECTIVE : Federated learning (FL) allows multiple distributed data holders to collaboratively learn a shared model without data sharing. However, individual health system data are heterogeneous. "Personalized" FL variations have been developed to counter data heterogeneity, but few have been evaluated using real-world healthcare data. The purpose of this study is to investigate the performance of a single-site versus a 3-client federated model using a previously described COVID-19 diagnostic model. Additionally, to investigate the effect of system heterogeneity, we evaluate the performance of 4 FL variations.

MATERIALS AND METHODS : We leverage a FL healthcare collaborative including data from 5 international healthcare systems (US and Europe) encompassing 42 hospitals. We implemented a COVID-19 computer vision diagnosis system using the FedAvg algorithm implemented on Clara Train SDK 4.0. To study the effect of data heterogeneity, training data was pooled from 3 systems locally and federation was simulated. We compared a centralized/pooled model, versus FedAvg, and 3 personalized FL variations (FedProx, FedBN, FedAMP).

RESULTS : We observed comparable model performance with respect to internal validation (local model: AUROC 0.94 vs FedAvg: 0.95, p = 0.5) and improved model generalizability with the FedAvg model (p < 0.05). When investigating the effects of model heterogeneity, we observed poor performance with FedAvg on internal validation as compared to personalized FL algorithms. FedAvg did have improved generalizability compared to personalized FL algorithms. On average, FedBN had the best rank performance on internal and external validation.

CONCLUSION : FedAvg can significantly improve the generalization of the model compared to other personalization FL algorithms; however, at the cost of poor internal validity. Personalized FL may offer an opportunity to develop both internal and externally validated algorithms.

Peng Le, Luo Gaoxiang, Walker Andrew, Zaiman Zachary, Jones Emma K, Gupta Hemant, Kersten Kristopher, Burns John L, Harle Christopher A, Magoc Tanja, Shickel Benjamin, Steenburg Scott D, Loftus Tyler, Melton Genevieve B, Gichoya Judy Wawira, Sun Ju, Tignanelli Christopher J

2022-Oct-10

Artificial Intelligence, COVID-19, Computer Vision, Federated Learning

General General

MO-MEMES: A method for accelerating virtual screening using multi-objective Bayesian optimization.

In Frontiers in medicine

The pursuit of potential inhibitors for novel targets has become a very important problem especially over the last 2 years with the world in the midst of the COVID-19 pandemic. This entails performing high throughput screening exercises on drug libraries to identify potential "hits". These hits are identified using analysis of their physical properties like binding affinity to the target receptor, octanol-water partition coefficient (LogP) and more. However, drug libraries can be extremely large and it is infeasible to calculate and analyze the physical properties for each of those molecules within acceptable time and moreover, each molecule must possess a multitude of properties apart from just the binding affinity. To address this problem, in this study, we propose an extension to the Machine learning framework for Enhanced MolEcular Screening (MEMES) framework for multi-objective Bayesian optimization. This approach is capable of identifying over 90% of the most desirable molecules with respect to all required properties while explicitly calculating the values of each of those properties on only 6% of the entire drug library. This framework would provide an immense boost in identifying potential hits that possess all properties required for a drug molecules.

Mehta Sarvesh, Goel Manan, Priyakumar U Deva

2022

Bayesian optimization, High throughout screening, chemical space exploration, drug discovery, machine learning, virtual screening

General General

An implementation of a hybrid method based on machine learning to identify biomarkers in the Covid-19 diagnosis using DNA sequences.

In Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society

Although some people do not have any chronic disease or are not in the risky age group for Covid-19, they are more vulnerable to the coronavirus. As the reason for this situation, some experts focus on the immune system of the person, while others think that the genetic history of patients may play a role. It is critical to detect corona from DNA signals as early as possible to determine the relationship between Covid-19 and genes. Thus, the effect on the severe course of the disease of variations in the genes associated with the corona disease will be revealed. In this study, a novel intelligent computer approach is proposed to identify coronavirus from nucleotide signals for the first time. The proposed method presents a multilayered feature extraction structure to extract the most effective features using an Entropy-based mapping technique, Discrete Wavelet Transform (DWT), statistical feature extractor, and Singular Value Decomposition (SVD), together. Then 94 distinctive features are selected by the ReliefF technique. Support vector machine (SVM) and k nearest neighborhood (k-NN) are chosen as classifiers. The method achieved the highest classification accuracy rate of 98.84% with an SVM classifier to detect Covid-19 from DNA signals. The proposed method is ready to be tested with a different database in the diagnosis of Covid-19 using RNA or other signals.

Das Bihter

2022-Nov-15

Big data analysis, Biomedical signal processing, Covid-19, Linear algebra, Machine learning

General General

Audits and COVID-19: A paradigm shift in the making.

In Business horizons

The COVID-19 pandemic has exposed the obsolescence and vulnerability of many existing auditing practices. Whilst some progressive practices have been implemented (i.e., remote audits using rudimentary Information & Communication Technologies), a new paradigm is needed to not only account for the risk of repeated lockdowns, but also to align practices with the level of digitalization, automation, and use of artificial intelligence in the current business environment. In this paper, we argue that the adoption of new technologies requires a fundamental rethinking of how auditing services are delivered. We argue that new technological possibilities have implications for five other auditing elements that enable a shift from the old to the new paradigm of auditing, namely actors, processes, spaces, training and skills development, and services. We explain how non-financial audits conducted under the new paradigm are key enablers of a firm's ability to participate and thrive in a competitive international marketplace.

Castka Pavel, Searcy Cory

2021-Nov-18

Audit, Certification, Inspection, Technology, Testing

Public Health Public Health

Identification of methylation signatures and rules for predicting the severity of SARS-CoV-2 infection with machine learning methods.

In Frontiers in microbiology

Patients infected with SARS-CoV-2 at various severities have different clinical manifestations and treatments. Mild or moderate patients usually recover with conventional medical treatment, but severe patients require prompt professional treatment. Thus, stratifying infected patients for targeted treatment is meaningful. A computational workflow was designed in this study to identify key blood methylation features and rules that can distinguish the severity of SARS-CoV-2 infection. First, the methylation features in the expression profile were deeply analyzed by a Monte Carlo feature selection method. A feature list was generated. Next, this ranked feature list was fed into the incremental feature selection method to determine the optimal features for different classification algorithms, thereby further building optimal classifiers. These selected key features were analyzed by functional enrichment to detect their biofunctional information. Furthermore, a set of rules were set up by a white-box algorithm, decision tree, to uncover different methylation patterns on various severity of SARS-CoV-2 infection. Some genes (PARP9, MX1, IRF7), corresponding to essential methylation sites, and rules were validated by published academic literature. Overall, this study contributes to revealing potential expression features and provides a reference for patient stratification. The physicians can prioritize and allocate health and medical resources for COVID-19 patients based on their predicted severe clinical outcomes.

Liu Zhiyang, Meng Mei, Ding ShiJian, Zhou XiaoChao, Feng KaiYan, Huang Tao, Cai Yu-Dong

2022

SARS-CoV-2, classification rule, machine learning, methylation, severity

General General

Machine learning-based derivation and external validation of a tool to predict death and development of organ failure in hospitalized patients with COVID-19.

In Scientific reports ; h5-index 158.0

COVID-19 mortality risk stratification tools could improve care, inform accurate and rapid triage decisions, and guide family discussions regarding goals of care. A minority of COVID-19 prognostic tools have been tested in external cohorts. Our objective was to compare machine learning algorithms and develop a tool for predicting subsequent clinical outcomes in COVID-19. We conducted a retrospective cohort study that included hospitalized patients with COVID-19 from March 2020 to March 2021. Seven Hundred Twelve consecutive patients from University of Washington and 345 patients from Tongji Hospital in China were included. We applied three different machine learning algorithms to clinical and laboratory data collected within the initial 24 h of hospital admission to determine the risk of in-hospital mortality, transfer to the intensive care unit, shock requiring vasopressors, and receipt of renal replacement therapy. Mortality risk models were derived, internally validated in UW and externally validated in Tongji Hospital. The risk models for ICU transfer, shock and RRT were derived and internally validated in the UW dataset but were unable to be externally validated due to a lack of data on these outcomes. Among the UW dataset, 122 patients died (17%) during hospitalization and the mean days to hospital mortality was 15.7 +/- 21.5 (mean +/- SD). Elastic net logistic regression resulted in a C-statistic for in-hospital mortality of 0.72 (95% CI, 0.64 to 0.81) in the internal validation and 0.85 (95% CI, 0.81 to 0.89) in the external validation set. Age, platelet count, and white blood cell count were the most important predictors of mortality. In the sub-group of patients > 50 years of age, the mortality prediction model continued to perform with a C-statistic of 0.82 (95% CI:0.76,0.87). Prediction models also performed well for shock and RRT in the UW dataset but functioned with lower accuracy for ICU transfer. We trained, internally and externally validated a prediction model using data collected within 24 h of hospital admission to predict in-hospital mortality on average two weeks prior to death. We also developed models to predict RRT and shock with high accuracy. These models could be used to improve triage decisions, resource allocation, and support clinical trial enrichment.

Xu Yixi, Trivedi Anusua, Becker Nicholas, Blazes Marian, Ferres Juan Lavista, Lee Aaron, Conrad Liles W, Bhatraju Pavan K

2022-Oct-08

General General

The COVIDTW study: Clinical predictors of COVID-19 mortality and a novel AI prognostic model using chest X-ray.

In Journal of the Formosan Medical Association = Taiwan yi zhi

BACKGROUND : There is a lack of published research on the impact of the first wave of the COVID-19 pandemic in Taiwan. We investigated the mortality risk factors among critically ill patients with COVID-19 in Taiwan during the initial wave. Furthermore, we aim to develop a novel AI mortality prediction model using chest X-ray (CXR) alone.

METHOD : We retrospectively reviewed the medical records of patients with COVID-19 at Taipei Tzu Chi Hospital from May 15 to July 15 2021. We enrolled adult patients who received invasive mechanical ventilation. The CXR images of each enrolled patient were divided into 4 categories (1st, pre-ETT, ETT, and WORST). To establish a prediction model, we used the MobilenetV3-Small model with "Imagenet" pretrained weights, followed by high Dropout regularization layers. We trained the model with these data with Five-Fold Cross-Validation to evaluate model performance.

RESULT : A total of 64 patients were enrolled. The overall mortality rate was 45%. The median time from symptom onset to intubation was 8 days. Vasopressor use and a higher BRIXIA score on the WORST CXR were associated with an increased risk of mortality. The areas under the curve of the 1st, pre-ETT, ETT, and WORST CXRs by the AI model were 0.87, 0.92, 0.96, and 0.93 respectively.

CONCLUSION : The mortality rate of COVID-19 patients who receive invasive mechanical ventilation was high. Septic shock and high BRIXIA score were clinical predictors of mortality. The novel AI mortality prediction model using CXR alone exhibited a high performance.

Wu Chih-Wei, Pham Bach-Tung, Wang Jia-Ching, Wu Yao-Kuang, Kuo Chan-Yen, Hsu Yi-Chiung

2022-Sep-26

Artificial intelligence, COVID-19, Chest X-rays, Intensive care unit, Mortality, Prognosis

General General

Impact of Covid-19 on research and training in Parkinson's disease.

In International review of neurobiology

The Coronavirus Disease 2019 (Covid-19) pandemic and the consequent restrictions imposed worldwide have posed an unprecedented challenge to research and training in Parkinson's disease (PD). The pandemic has caused loss of productivity, reduced access to funding, an oft-acute switch to digital platforms, and changes in daily work protocols, or even redeployment. Frequently, clinical and research appointments were suspended or changed as a solution to limit the risk of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) spread and infection, but since the care and research in the field of movement disorders had traditionally been performed at in-person settings, the repercussions of the pandemic have even been more keenly felt in these areas. In this chapter, we review the implications of this impact on neurological research and training, with an emphasis on PD, as well as highlight lessons that can be learnt from how the Covid-19 pandemic has been managed in terms of restrictions in these crucial aspects of the neurosciences. One of the solutions brought to the fore has been to replace the traditional way of performing research and training with remote, and therefore socially distanced, alternatives. However, this has introduced fresh challenges in international collaboration, contingency planning, study prioritization, safety precautions, artificial intelligence, and various forms of digital technology. Nonetheless, in the long-term, these strategies will allow us to mitigate the adverse impact on PD research and training in future crises.

Wan Yi-Min, van Wamelen Daniel J, Lau Yue Hui, Rota Silvia, Tan Eng-King

2022

Covid-19, Impact, Movement disorders, “Parkinsons disease”, Research, Training

General General

Mathematical modeling and AI based decision making for COVID-19 suspects backed by novel distance and similarity measures on plithogenic hypersoft sets.

In Artificial intelligence in medicine ; h5-index 34.0

It goes without saying that coronavirus (COVID-19) is an infectious disease and many countries are coping with its different variants. Owing to the limited medical facilities, vaccine and medical experts, need of the hour is to intelligently tackle its spread by making artificial intelligence (AI) based smart decisions for COVID-19 suspects who develop different symptoms and they are kept under observation and monitored to see the severity of the symptoms. The target of this study is to analyze COVID-19 suspects data and detect whether a suspect is a COVID-19 patient or not, and if yes, then to what extent, so that a suitable decision can be made. The decision can be categorized such that an infected person can be isolated or quarantined at home or at a facilitation center or the person can be sent to the hospital for the treatment. This target is achieved by designing a mathematical model of COVID-19 suspects in the form of a multi-criteria decision making (MCDM) model and a novel AI based technique is devised and implemented with the help of newly developed plithogenic distance and similarity measures in fuzzy environment. All findings are depicted graphically for a clear understanding and to provide an insight of the necessity and effectiveness of the proposed method. The concept and results of the proposed technique make it suitable for implementation in machine learning, deep learning, pattern recognition etc.

Ahmad Muhammad Rayees, Afzal Usman

2022-Oct

COVID-19, Multi-criteria decision making (MCDM), Plithogenic distance measure (PDM), Plithogenic hypersoft set (PHSS), Plithogenic similarity measure (PSM)

General General

Defining factors in hospital admissions during COVID-19 using LSTM-FCA explainable model.

In Artificial intelligence in medicine ; h5-index 34.0

Outbreaks of the COVID-19 pandemic caused by the SARS-CoV-2 infection that started in Wuhan, China, have quickly spread worldwide. The current situation has contributed to a dynamic rate of hospital admissions. Global efforts by Artificial Intelligence (AI) and Machine Learning (ML) communities to develop solutions to assist COVID-19-related research have escalated ever since. However, despite overwhelming efforts from the AI and ML community, many machine learning-based AI systems have been designed as black boxes. This paper proposes a model that utilizes Formal Concept Analysis (FCA) to explain a machine learning technique called Long-short Term Memory (LSTM) on a dataset of hospital admissions due to COVID-19 in the United Kingdom. This paper intends to increase the transparency of decision-making in the era of ML by using the proposed LSTM-FCA explainable model. Both LSTM and FCA are able to evaluate the data and explain the model to make the results more understandable and interpretable. The results and discussions are helpful and may lead to new research to optimize the use of ML in various real-world applications and to contain the disease.

Md Saleh Nurul Izrin, Ab Ghani Hadhrami, Jilani Zairul

2022-Oct

COVID-19, Formal Concept Analysis (FCA), Hospital admissions, Long Short-Term Memory (LSTM)

General General

A self-supervised COVID-19 CT recognition system with multiple regularizations.

In Computers in biology and medicine

The diagnosis of Coronavirus Disease 2019 (COVID-19) exploiting machine learning algorithms based on chest computed tomography (CT) images has become an important technology. Though many excellent computer-aided methods leveraging CT images have been designed, they do not possess sufficiently high recognition accuracy. Besides, these methods entail vast amounts of training data, which might be difficult to be satisfied in some real-world applications. To address these two issues, this paper proposes a novel COVID-19 recognition system based on CT images, which has high recognition accuracy, while only requiring a small amount of training data. Specifically, the system possesses the following three improvements: 1) Data: a novel redesigned BCELoss that incorporates Label Smoothing, Focal Loss, and Label Weighting Regularization (LSFLLW-R) technique for optimizing the solution space and preventing overfitting, 2) Model: a backbone network processed by two-phase contrastive self-supervised learning for classifying multiple labels, and 3) Method: a decision-fusing ensemble learning method for getting a more stable system, with balanced metric values. Our proposed system is evaluated on the small-scale expanded COVID-CT dataset, achieving an accuracy of 94.3%, a precision of 94.1%, a recall (sensitivity) of 93.4%, an F1-score of 94.7%, and an Area Under the Curve (AUC) of 98.9%, for COVID-19 diagnosis, respectively. These experimental results verify that our system can not only identify pathological locations effectively, but also achieve better performance in terms of accuracy, generalizability, and stability, compared with several other state-of-the-art COVID-19 diagnosis methods.

Lu Han, Dai Qun

2022-Sep-29

COVID-19 CT Diagnosis, Contrastive learning, Deep neural network, Ensemble learning, Loss regularization

Radiology Radiology

RadImageNet: An Open Radiologic Deep Learning Research Dataset for Effective Transfer Learning.

In Radiology. Artificial intelligence

Purpose : To demonstrate the value of pretraining with millions of radiologic images compared with ImageNet photographic images on downstream medical applications when using transfer learning.

Materials and Methods : This retrospective study included patients who underwent a radiologic study between 2005 and 2020 at an outpatient imaging facility. Key images and associated labels from the studies were retrospectively extracted from the original study interpretation. These images were used for RadImageNet model training with random weight initiation. The RadImageNet models were compared with ImageNet models using the area under the receiver operating characteristic curve (AUC) for eight classification tasks and using Dice scores for two segmentation problems.

Results : The RadImageNet database consists of 1.35 million annotated medical images in 131 872 patients who underwent CT, MRI, and US for musculoskeletal, neurologic, oncologic, gastrointestinal, endocrine, abdominal, and pulmonary pathologic conditions. For transfer learning tasks on small datasets-thyroid nodules (US), breast masses (US), anterior cruciate ligament injuries (MRI), and meniscal tears (MRI)-the RadImageNet models demonstrated a significant advantage (P < .001) to ImageNet models (9.4%, 4.0%, 4.8%, and 4.5% AUC improvements, respectively). For larger datasets-pneumonia (chest radiography), COVID-19 (CT), SARS-CoV-2 (CT), and intracranial hemorrhage (CT)-the RadImageNet models also illustrated improved AUC (P < .001) by 1.9%, 6.1%, 1.7%, and 0.9%, respectively. Additionally, lesion localizations of the RadImageNet models were improved by 64.6% and 16.4% on thyroid and breast US datasets, respectively.

Conclusion : RadImageNet pretrained models demonstrated better interpretability compared with ImageNet models, especially for smaller radiologic datasets.Keywords: CT, MR Imaging, US, Head/Neck, Thorax, Brain/Brain Stem, Evidence-based Medicine, Computer Applications-General (Informatics) Supplemental material is available for this article. Published under a CC BY 4.0 license.See also the commentary by Cadrin-Chênevert in this issue.

Mei Xueyan, Liu Zelong, Robson Philip M, Marinelli Brett, Huang Mingqian, Doshi Amish, Jacobi Adam, Cao Chendi, Link Katherine E, Yang Thomas, Wang Ying, Greenspan Hayit, Deyer Timothy, Fayad Zahi A, Yang Yang

2022-Sep

Brain/Brain Stem, CT, Computer Applications–General (Informatics), Evidence-based Medicine, Head/Neck, MR Imaging, Thorax, US

General General

Difference in mortality rates in hospitalized COVID-19 patients identified by cytokine profile clustering using a machine learning approach: An outcome prediction alternative.

In Frontiers in medicine

COVID-19 is a disease caused by the novel Coronavirus SARS-CoV-2 causing an acute respiratory disease that can eventually lead to severe acute respiratory syndrome (SARS). An exacerbated inflammatory response is characteristic of SARS-CoV-2 infection, which leads to a cytokine release syndrome also known as cytokine storm associated with the severity of the disease. Considering the importance of this event in the immunopathology of COVID-19, this study analyses cytokine levels of hospitalized patients to identify cytokine profiles associated with severity and mortality. Using a machine learning approach, 3 clusters of COVID-19 hospitalized patients were created based on their cytokine profile. Significant differences in the mortality rate were found among the clusters, associated to different CXCL10/IL-38 ratio. The balance of a CXCL10 induced inflammation with an appropriate immune regulation mediated by the anti-inflammatory cytokine IL-38 appears to generate the adequate immune context to overrule SARS-CoV-2 infection without creating a harmful inflammatory reaction. This study supports the concept that analyzing a single cytokine is insufficient to determine the outcome of a complex disease such as COVID-19, and different strategies incorporating bioinformatic analyses considering a broader immune profile represent a more robust alternative to predict the outcome of hospitalized patients with SARS-CoV-2 infection.

Castro-Castro Ana Cristina, Figueroa-Protti Lucia, Molina-Mora Jose Arturo, Rojas-Salas María Paula, Villafuerte-Mena Danae, Suarez-Sánchez María José, Sanabría-Castro Alfredo, Boza-Calvo Carolina, Calvo-Flores Leonardo, Solano-Vargas Mariela, Madrigal-Sánchez Juan José, Sibaja-Campos Mario, Silesky-Jiménez Juan Ignacio, Chaverri-Fernández José Miguel, Soto-Rodríguez Andrés, Echeverri-McCandless Ann, Rojas-Chaves Sebastián, Landaverde-Recinos Denis, Weigert Andreas, Mora Javier

2022

COVID-19, CXCL10, IL-38, SARS-CoV-2, cytokine profile

General General

Blood gene expression predicts intensive care unit admission in hospitalised patients with COVID-19.

In Frontiers in immunology ; h5-index 100.0

Background : The COVID-19 pandemic has created pressure on healthcare systems worldwide. Tools that can stratify individuals according to prognosis could allow for more efficient allocation of healthcare resources and thus improved patient outcomes. It is currently unclear if blood gene expression signatures derived from patients at the point of admission to hospital could provide useful prognostic information.

Methods : Gene expression of whole blood obtained at the point of admission from a cohort of 78 patients hospitalised with COVID-19 during the first wave was measured by high resolution RNA sequencing. Gene signatures predictive of admission to Intensive Care Unit were identified and tested using machine learning and topological data analysis, TopMD.

Results : The best gene expression signature predictive of ICU admission was defined using topological data analysis with an accuracy: 0.72 and ROC AUC: 0.76. The gene signature was primarily based on differentially activated pathways controlling epidermal growth factor receptor (EGFR) presentation, Peroxisome proliferator-activated receptor alpha (PPAR-α) signalling and Transforming growth factor beta (TGF-β) signalling.

Conclusions : Gene expression signatures from blood taken at the point of admission to hospital predicted ICU admission of treatment naïve patients with COVID-19.

Penrice-Randal Rebekah, Dong Xiaofeng, Shapanis Andrew George, Gardner Aaron, Harding Nicholas, Legebeke Jelmer, Lord Jenny, Vallejo Andres F, Poole Stephen, Brendish Nathan J, Hartley Catherine, Williams Anthony P, Wheway Gabrielle, Polak Marta E, Strazzeri Fabio, Schofield James P R, Skipp Paul J, Hiscox Julian A, Clark Tristan W, Baralle Diana

2022

COVID-19, Critical Care, RNA-seq - RNA sequencing, biomarkers, prognosis, topology, transcriptome

General General

Hospital trajectories and early predictors of clinical outcomes differ between SARS-CoV-2 and influenza pneumonia.

In EBioMedicine

BACKGROUND : A comparison of pneumonias due to SARS-CoV-2 and influenza, in terms of clinical course and predictors of outcomes, might inform prognosis and resource management. We aimed to compare clinical course and outcome predictors in SARS-CoV-2 and influenza pneumonia using multi-state modelling and supervised machine learning on clinical data among hospitalised patients.

METHODS : This multicenter retrospective cohort study of patients hospitalised with SARS-CoV-2 (March-December 2020) or influenza (Jan 2015-March 2020) pneumonia had the composite of hospital mortality and hospice discharge as the primary outcome. Multi-state models compared differences in oxygenation/ventilatory utilisation between pneumonias longitudinally throughout hospitalisation. Differences in predictors of outcome were modelled using supervised machine learning classifiers.

FINDINGS : Among 2,529 hospitalisations with SARS-CoV-2 and 2,256 with influenza pneumonia, the primary outcome occurred in 21% and 9%, respectively. Multi-state models differentiated oxygen requirement progression between viruses, with SARS-CoV-2 manifesting rapidly-escalating early hypoxemia. Highly contributory classifier variables for the primary outcome differed substantially between viruses.

INTERPRETATION : SARS-CoV-2 and influenza pneumonia differ in presentation, hospital course, and outcome predictors. These pathogen-specific differential responses in viral pneumonias suggest distinct management approaches should be investigated.

FUNDING : This project was supported by NIH/NCATS UL1 TR002345, NIH/NCATS KL2 TR002346 (PGL), the Doris Duke Charitable Foundation grant 2015215 (PGL), NIH/NHLBI R35 HL140026 (CSC), and a Big Ideas Award from the BJC HealthCare and Washington University School of Medicine Healthcare Innovation Lab and NIH/NIGMS R35 GM142992 (PS).

Lyons Patrick G, Bhavani Sivasubramanium V, Mody Aaloke, Bewley Alice, Dittman Katherine, Doyle Aisling, Windham Samuel L, Patel Tej M, Raju Bharat Neelam, Keller Matthew, Churpek Matthew M, Calfee Carolyn S, Michelson Andrew P, Kannampallil Thomas, Geng Elvin H, Sinha Pratik

2022-Oct-03

Hospital outcomes, Influenza, SARS-CoV-2, Statistical modelling, Viral pneumonia

Ophthalmology Ophthalmology

Deep learning models for COVID-19 chest x-ray classification: Preventing shortcut learning using feature disentanglement.

In PloS one ; h5-index 176.0

In response to the COVID-19 global pandemic, recent research has proposed creating deep learning based models that use chest radiographs (CXRs) in a variety of clinical tasks to help manage the crisis. However, the size of existing datasets of CXRs from COVID-19+ patients are relatively small, and researchers often pool CXR data from multiple sources, for example, using different x-ray machines in various patient populations under different clinical scenarios. Deep learning models trained on such datasets have been shown to overfit to erroneous features instead of learning pulmonary characteristics in a phenomenon known as shortcut learning. We propose adding feature disentanglement to the training process. This technique forces the models to identify pulmonary features from the images and penalizes them for learning features that can discriminate between the original datasets that the images come from. We find that models trained in this way indeed have better generalization performance on unseen data; in the best case we found that it improved AUC by 0.13 on held out data. We further find that this outperforms masking out non-lung parts of the CXRs and performing histogram equalization, both of which are recently proposed methods for removing biases in CXR datasets.

Trivedi Anusua, Robinson Caleb, Blazes Marian, Ortiz Anthony, Desbiens Jocelyn, Gupta Sunil, Dodhia Rahul, Bhatraju Pavan K, Liles W Conrad, Kalpathy-Cramer Jayashree, Lee Aaron Y, Lavista Ferres Juan M

2022

General General

Neural-Symbolic Ensemble Learning for early-stage prediction of critical state of Covid-19 patients.

In Medical & biological engineering & computing ; h5-index 32.0

Recently, Artificial Intelligence (AI) and Machine Learning (ML) have been successfully applied to many domains of interest including medical diagnosis. Due to the availability of a large quantity of data, it is possible to build reliable AI systems that assist humans in making decisions. The recent Covid-19 pandemic quickly spread over the world causing serious health problems and severe economic and social damage. Computer scientists are actively working together with doctors on different ML models to diagnose Covid-19 patients using Computed Tomography (CT) scans and clinical data. In this work, we propose a neural-symbolic system that predicts if a Covid-19 patient arriving at the hospital will end in a critical condition. The proposed system relies on Deep 3D Convolutional Neural Networks (3D-CNNs) for analyzing lung CT scans of Covid-19 patients, Decision Trees (DTs) for predicting if a Covid-19 patient will eventually pass away by analyzing its clinical data, and a neural system that integrates the previous ones using Hierarchical Probabilistic Logic Programs (HPLPs). Predicting if a Covid-19 patient will end in a critical condition is useful for managing the limited number of intensive care at the hospital. Moreover, knowing early that a Covid-19 patient could end in serious conditions allows doctors to gain early knowledge on patients and provide special treatment to those predicted to finish in critical conditions. The proposed system, entitled Neural HPLP, obtains good performance in terms of area under the receiver operating characteristic and precision curves with values of about 0.96 for both metrics. Therefore, with Neural HPLP, it is possible not only to efficiently predict if Covid-19 patients will end in severe conditions but also possible to provide an explanation of the prediction. This makes Neural HPLP explainable, interpretable, and reliable. Graphical abstract Representation of Neural HPLP. From top to bottom, the two different types of data collected from the same patient and used in this project are represented. This data feeds the two different machine learning systems and the integration of the two systems using Hierarchical Probabilistic Logic Program.

Fadja Arnaud Nguembang, Fraccaroli Michele, Bizzarri Alice, Mazzuchelli Giulia, Lamma Evelina

2022-Oct-06

Covid-19, Decision Trees, Deep Learning, Hierarchical Probabilistic Logic Program, Severity

General General

Monkeypox Virus Detection Using Pre-trained Deep Learning-based Approaches.

In Journal of medical systems ; h5-index 48.0

Monkeypox virus is emerging slowly with the decline of COVID-19 virus infections around the world. People are afraid of it, thinking that it would appear as a pandemic like COVID-19. As such, it is crucial to detect them earlier before widespread community transmission. AI-based detection could help identify them at the early stage. In this paper, we aim to compare 13 different pre-trained deep learning (DL) models for the Monkeypox virus detection. For this, we initially fine-tune them with the addition of universal custom layers for all of them and analyse the results using four well-established measures: Precision, Recall, F1-score, and Accuracy. After the identification of the best-performing DL models, we ensemble them to improve the overall performance using a majority voting over the probabilistic outputs obtained from them. We perform our experiments on a publicly available dataset, which results in average Precision, Recall, F1-score, and Accuracy of 85.44%, 85.47%, 85.40%, and 87.13%, respectively with the help of our proposed ensemble approach. These encouraging results, which outperform the state-of-the-art methods, suggest that the proposed approach is applicable to health practitioners for mass screening.

Sitaula Chiranjibi, Shahi Tej Bahadur

2022-Oct-06

Classification, Deep learning, Detection, Monkeypox, Pandemic, SARS-Cov2

oncology Oncology

Exploration of the Potential Link, Hub Genes, and Potential Drugs for Coronavirus Disease 2019 and Lung Cancer Based on Bioinformatics Analysis.

In Journal of oncology

The ongoing pandemic of coronavirus disease 2019 (COVID-19) has a huge influence on global public health and the economy. Lung cancer is one of the high-risk factors of COVID-19, but the molecular mechanism of lung cancer and COVID-19 is still unclear, and further research is needed. Therefore, we used the transcriptome information of the public database and adopted bioinformatics methods to identify the common pathways and molecular biomarkers of lung cancer and COVID-19 to further understand the connection between them. The two RNA-seq data sets in this study-GSE147507 (COVID-19) and GSE33532 (lung cancer)-were both derived from the Gene Expression Omnibus (GEO) database and identified differentially expressed genes (DEGs) for lung cancer and COVID-19 patients. We conducted Gene Ontology (GO) functions and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis and found some common features between lung cancer and COVID-19. We also performed TFs-gene, miRNAs-gene, and gene-drug analyses. In total, 32 DEGs were found. A protein-protein interaction (PPI) network was constructed by DEGs, and 10 hub genes were screened. Finally, the identified drugs may be helpful for COVID-19 treatment.

Wang Ye, Li Qing, Zhang Jianfang, Xie Hui

2022

General General

An integrated and automated testing approach on Inception Restnet-V3 based on convolutional neural network for leukocytes image classification.

In Biomedizinische Technik. Biomedical engineering

OBJECTIVES : The leukocyte is a specialized immune cell that functions as the foundation of the immune system and keeps the body healthy. The WBC classification plays a vital role in diagnosing various disorders in the medical area, including infectious diseases, immune deficiencies, leukemia, and COVID-19. A few decades ago, Machine Learning algorithms classified WBC types required for image segmentation, and the feature extraction stages, but this new approach becomes automatic while existing models can be fine-tuned for specific classifications.

METHODS : The inception architecture and deep learning model-based Resnet connection are integrated into this article. Our proposed method, inception Resnet-v3, was used to classify WBCs into five categories using 15.7k images. Pathologists made diagnoses of all images so a model could be trained to classify five distinct types of cells.

RESULTS : After implementing the proposed architecture on a large dataset of 5 categories of human peripheral white blood cells, it achieved high accuracy than VGG, U-Net and Resnet. We tested our model with WBC images from additional public datasets such as the Kaagel data sets and Raabin data sets of which the accuracy was 98.80% and 98.95%.

CONCLUSIONS : Considering the large sample sizes, we believe the proposed method can be used for improving the diagnostic performance of clinical blood examinations as well as a promising alternative for machine learning. Test results obtained with the system have been satisfying, with outstanding values for Accuracy, Precision, Recall, Specificity and F1 Score.

Palanivel Silambarasi, Nallasamy Viswanathan

2022-Oct-05

deep learning, image classification, inception V3, leukocyte, residual network

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