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Public Health Public Health

Identifying knowledge gaps in heart failure research among women using unsupervised machine-learning methods.

In Future cardiology

Aim: To identify knowledge gaps in heart failure (HF) research among women, especially postmenopausal women. Materials & methods: We retrieved HF articles from PubMed. Natural language processing and text mining techniques were used to screen relevant articles and identify study objective(s) from abstracts. After text preprocessing, we performed topic modeling with non-negative matrix factorization to cluster articles based on the primary topic. Clusters were independently validated and labeled by three investigators familiar with HF research. Results: Our model yielded 15 topic clusters from articles on HF among women. Atrial fibrillation was found to be the most understudied topic. From articles specific to postmenopausal women, five clusters were identified. The smallest cluster was about stress-induced cardiomyopathy. Conclusion: Topic modeling can help identify understudied areas in medical research.

Alhussain Khalid, Kido Kazuhiko, Dwibedi Nilanjana, LeMasters Traci, Rose Danielle E, Misra Ranjita, Sambamoorthi Usha

2021-Jan-11

heart failure research, postmenopausal women, topic modeling, unsupervised learning, women

Radiology Radiology

Investigating the feasibility of generating dual-energy CT from one 120-kVp CT scan: a phantom study.

In Journal of applied clinical medical physics ; h5-index 28.0

INTRODUCTION : This study aimed to investigate the feasibility of generating pseudo dual-energy CT (DECT) from one 120-kVp CT by using convolutional neural network (CNN) to derive additional information for quantitative image analysis through phantom study.

METHODS : Dual-energy scans (80/140 kVp) and single-energy scans (120 kVp) were performed for five calibration phantoms and two evaluation phantoms on a dual-source DECT scanner. The calibration phantoms were used to generate training dataset for CNN optimization, while the evaluation phantoms were used to generate testing dataset. A CNN model which takes 120-kVp images as input and creates 80/140-kVp images as output was built, trained, and tested by using Caffe CNN platform. An in-house software to quantify contrast enhancement and synthesize virtual monochromatic CT (VMCT) for CNN-generated pseudo DECT was implemented and evaluated.

RESULTS : The CT numbers in 80-kVp pseudo images generated by CNN are differed from the truth by 11.57, 16.67, 13.92, 12.23, 10.69 HU for syringes filled with iodine concentration of 2.19, 4.38, 8.75, 17.5, 35 mg/ml, respectively. The corresponding results for 140-kVp CT are 3.09, 9.10, 7.08, 9.81, 7.59 HU. The estimates of iodine concentration calculated based on the proposed method are differed from the truth by 0.104, 0.603, 0.478, 0.698, 0.795 mg/ml for syringes filled with iodine concentration of 2.19, 4.38, 8.75, 17.5, 35 mg/ml, respectively. With regards to image quality enhancement, VMCT synthesized by using pseudo DECT shows the best contrast-to-noise ratio at 40 keV.

CONCLUSION : In conclusion, the proposed method should be a practicable strategy for iodine quantification in contrast enhanced 120-kVp CT without using specific scanner or scanning procedure.

Huang Wen-Hui, Jhan Kai-Jie, Yang Ching-Ching

2020-Oct-14

deep learning, dual energy CT, pseudo CT

General General

Contemporary training methods in regional anaesthesia: fundamentals and innovations.

In Anaesthesia ; h5-index 53.0

Over the past two decades, regional anaesthesia and medical education as a whole have undergone a renaissance. Significant changes in our teaching methods and clinical practice have been influenced by improvements in our theoretical understanding as well as by technological innovations. More recently, there has been a focus on using foundational education principles to teach regional anaesthesia, and the evidence on how to best teach and assess trainees is growing. This narrative review will discuss fundamentals and innovations in regional anaesthesia training. We present the fundamentals in regional anaesthesia training, specifically the current state of simulation-based education, deliberate practice and curriculum design based on competency-based progression. Moving into the future, we present the latest innovations in web-based learning, emerging technologies for teaching and assessment and new developments in alternate reality learning systems.

Ramlogan R R, Chuan A, Mariano E R

2021-01

artificial intelligence, competency-based training, medical education, regional anaesthesia, simulation, technology

General General

Automatic Lung Health Screening Using Respiratory Sounds.

In Journal of medical systems ; h5-index 48.0

Significant changes have been made on audio-based technologies over years in several different fields. Healthcare is no exception. One of such avenues is health screening based on respiratory sounds. In this paper, we developed a tool to detect respiratory sounds that come from respiratory infection carrying patients. Linear Predictive Cepstral Coefficient (LPCC)-based features were used to characterize such audio clips. With Multilayer Perceptron (MLP)-based classifier, in our experiment, we achieved the highest possible accuracy of 99.22% that was tested on a publicly available respiratory sounds dataset (ICBHI17) (Rocha et al. Physiol. Meas. 40(3):035,001 20) of size 6800+ clips. In addition to other popular machine learning classifiers, our results outperformed common works that exist in the literature.

Mukherjee Himadri, Sreerama Priyanka, Dhar Ankita, Obaidullah Sk Md, Roy Kaushik, Mahmud Mufti, Santosh K C

2021-Jan-11

Healthcare, Lung health, Respiratory infection, Respiratory sound

Public Health Public Health

Non-alcoholic Fatty Liver and Liver Fibrosis Predictive Analytics: Risk Prediction and Machine Learning Techniques for Improved Preventive Medicine.

In Journal of medical systems ; h5-index 48.0

Non-alcoholic fatty liver disease (NAFLD) is the most common liver disease worldwide, with a prevalence of 20%-30% in the general population. NAFLD is associated with increased risk of cardiovascular disease and may progress to cirrhosis with time. The purpose of this study was to predict the risks associated with NAFLD and advanced fibrosis on the Fatty Liver Index (FLI) and the 'NAFLD fibrosis 4' calculator (FIB-4), to enable physicians to make more optimal preventive medical decisions. A prospective cohort of apparently healthy volunteers from the Tel Aviv Medical Center Inflammation Survey (TAMCIS), admitted for their routine annual health check-up. Data from the TAMCIS database were subjected to machine learning classification models to predict individual risk after extensive data preparation that included the computation of independent variables over several time points. After incorporating the time covariates and other key variables, this technique outperformed the predictive power of current popular methods (an improvement in AUC above 0.82). New powerful factors were identified during the predictive process. The findings can be used for risk stratification and in planning future preventive strategies based on lifestyle modifications and medical treatment to reduce the disease burden. Interventions to prevent chronic disease can substantially reduce medical complications and the costs of the disease. The findings highlight the value of predictive analytic tools in health care environments. NAFLD constitutes a growing burden on the health system; thus, identification of the factors related to its incidence can make a strong contribution to preventive medicine.

Goldman Orit, Ben-Assuli Ofir, Rogowski Ori, Zeltser David, Shapira Itzhak, Berliner Shlomo, Zelber-Sagi Shira, Shenhar-Tsarfaty Shani

2021-Jan-11

Machine learning, Non-alcoholic fatty liver disease, Predictive analytics, Risk prediction

General General

Big data augmentated business trend identification: the case of mobile commerce.

In Scientometrics

Identifying and monitoring business and technological trends are crucial for innovation and competitiveness of businesses. Exponential growth of data across the world is invaluable for identifying emerging and evolving trends. On the other hand, the vast amount of data leads to information overload and can no longer be adequately processed without the use of automated methods of extraction, processing, and generation of knowledge. There is a growing need for information systems that would monitor and analyse data from heterogeneous and unstructured sources in order to enable timely and evidence-based decision-making. Recent advancements in computing and big data provide enormous opportunities for gathering evidence on future developments and emerging opportunities. The present study demonstrates the use of text-mining and semantic analysis of large amount of documents for investigating in business trends in mobile commerce (m-commerce). Particularly with the on-going COVID-19 pandemic and resultant social isolation, m-commerce has become a large technology and business domain with ever growing market potentials. Thus, our study begins with a review of global challenges, opportunities and trends in the development of m-commerce in the world. Next, the study identifies critical technologies and instruments for the full utilization of the potentials in the sector by using the intelligent big data analytics system based on in-depth natural language processing utilizing text-mining, machine learning, science bibliometry and technology analysis. The results generated by the system can be used to produce a comprehensive and objective web of interconnected technologies, trends, drivers and barriers to give an overview of the whole landscape of m-commerce in one business intelligence (BI) data mart diagram.

Saritas Ozcan, Bakhtin Pavel, Kuzminov Ilya, Khabirova Elena

2021-Jan-05

COVID-19, Global trends, Horizon scanning, M-commerce, Machine learning, Natural language processing, Tech mining