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General General

Investigating mental health outcomes of undergraduates and graduate students in Taiwan during the COVID-19 pandemic.

In Journal of American college health : J of ACH

Objective: This study is an exploration of the major stressors associated with the COVID-19 for students in higher education in Taiwan. Participants: The sample comprised 838 higher education students studying at various Taiwanese universities. Methods: A cross-sectional online survey was administered at different postsecondary institutions during the semi-lockdown period of COVID-19, which mandated online instruction. Machine learning was employed to determine the variables that most highly predicted students' mental health using R. Results: The findings revealed that COVID-19-related experiences, including social interactions, financial conditions, and educational experiences, were significantly associated with mental health outcomes. Particularly, loneliness are significantly related to social interactions and educational experiences. Conclusions: Findings revealed that Covid-19 impacted Taiwanese students' financial conditions, educational experiences, and social interactions, which were significant predictors of their mental health outcomes such as anxiety, loneliness and depression. The current study contributes to the gap in knowledge about mental health issues among postsecondary students during the pandemic.

Lin Ching-Hui, Lin Szu-Yin, Hu Bo-Hsien, Lo C Owen

2023-Jan-03

College students, Covid-19, mental health, postsecondary education

General General

Users' Reactions on Announced Vaccines against COVID-19 Before Marketing in France: Analysis of Twitter posts.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Within a few months, the COVID-19 pandemic has spread to many countries and has been a real challenge for health systems all around the world. This unprecedented crisis has led to a surge of online discussions about potential cures for the disease. Among them, vaccines have been at the heart of the debates, and have faced lack of confidence before marketing in France.

OBJECTIVE : This study aims to identify and investigate the opinion of French Twitter users on the announced vaccines against COVID-19 through sentiment analysis.

METHODS : This study was conducted in two phases. First, we filtered a collection of tweets related to COVID-19 available on twitter from February to August 2020 with a set of keywords associated with vaccine mistrust using word embeddings. Second, we performed sentiment analysis using deep learning to identify the characteristics of vaccine mistrust. The model was trained on a hand labeled subset of 4,548 tweets.

RESULTS : A set of 69 relevant keywords were identified as the semantic concept of the word "vaccin" (vaccine in French) and focus mainly on conspiracies, pharmaceutical companies, and alternative treatments. Those keywords enabled to extract nearly 350k tweets in French. The sentiment analysis model achieved a 0.75 accuracy. The model then predicted 16% of positive tweets, 41% of negative tweets and 43% of neutral tweets. This allowed to explore the semantic concepts of positive and negative tweets and to plot the trends of each sentiment. The main negative rhetoric identified from users' tweets was that vaccines are perceived as having a political purpose, and that COVID-19 is a commercial argument for the pharmaceutical companies.

CONCLUSIONS : Twitter might be a useful tool to investigate the arguments of vaccine mistrust as it unveils a political criticism contrasting with the usual concerns on adverse drug reactions. As the opposition rhetoric is more consistent and more widely spread than the positive rhetoric, we believe that this research provides effective tools to help health authorities better characterize the risk of vaccine mistrust.

Dupuy-Zini Alexandre, Audeh Bissan, Gérardin Christel, Duclos Catherine, Gagneux-Brunon Amandine, Bousquet Cedric

2022-Aug-09

General General

Embedding-based link predictions to explore latent comorbidity of chronic diseases.

In Health information science and systems

PURPOSE : Comorbidity is a term used to describe when a patient simultaneously has more than one chronic disease. Comorbidity is a significant health issue that affects people worldwide. This study aims to use machine learning and graph theory to predict the comorbidity of chronic diseases.

METHODS : A patient-disease bipartite graph is constructed based on the administrative claim data. The bipartite graph projection approach was used to create the comorbidity network. For the link prediction task, three graph machine learning embedding-based models (node2vec, graph neural networks and hand-crafted approach) with different variants were used on the comorbidity network to compare their performance. This study also considered three commonly used similarity-based link prediction approaches (Jaccard coefficient, Adamic-Adar index and Resource allocation index) for performance comparison.

RESULTS : The results showed that the embedding-based hand-crafted features technique achieved outstanding performance compared with the remaining similarity-based and embedding-based models. Especially, the hand-crafted technique with the extreme gradient boosting classifier achieved the highest accuracy (91.67%), followed by the same technique with the Logistic regression classifier (90.26%). For this shallow embedding method, the Jaccard coefficient and the degree centrality of the original chronic disease were the most important features for comorbidity prediction.

CONCLUSION : The proposed framework can be used to predict the comorbidity of chronic disease at an early stage of hospital admission. Thus, the prediction outcome could be valuable for medical practice, giving healthcare providers more control over their services and lowering expenses.

Lu Haohui, Uddin Shahadat

2023-Dec

General General

COVID-19 Detection: A Systematic Review of Machine and Deep Learning-Based Approaches Utilizing Chest X-Rays and CT Scans.

In Cognitive computation

This review study presents the state-of-the-art machine and deep learning-based COVID-19 detection approaches utilizing the chest X-rays or computed tomography (CT) scans. This study aims to systematically scrutinize as well as to discourse challenges and limitations of the existing state-of-the-art research published in this domain from March 2020 to August 2021. This study also presents a comparative analysis of the performance of four majorly used deep transfer learning (DTL) models like VGG16, VGG19, ResNet50, and DenseNet over the COVID-19 local CT scans dataset and global chest X-ray dataset. A brief illustration of the majorly used chest X-ray and CT scan datasets of COVID-19 patients utilized in state-of-the-art COVID-19 detection approaches are also presented for future research. The research databases like IEEE Xplore, PubMed, and Web of Science are searched exhaustively for carrying out this survey. For the comparison analysis, four deep transfer learning models like VGG16, VGG19, ResNet50, and DenseNet are initially fine-tuned and trained using the augmented local CT scans and global chest X-ray dataset in order to observe their performance. This review study summarizes major findings like AI technique employed, type of classification performed, used datasets, results in terms of accuracy, specificity, sensitivity, F1 score, etc., along with the limitations, and future work for COVID-19 detection in tabular manner for conciseness. The performance analysis of the four majorly used deep transfer learning models affirms that Visual Geometry Group 19 (VGG19) model delivered the best performance over both COVID-19 local CT scans dataset and global chest X-ray dataset.

Bhatele Kirti Raj, Jha Anand, Tiwari Devanshu, Bhatele Mukta, Sharma Sneha, Mithora Muktasha R, Singhal Stuti

2022-Dec-29

COVID-19, CT scan, Chest X-ray, Deep transfer learning, Machine learning

oncology Oncology

Machine Learning and Nomogram Prognostic Modeling for 2-Year Head and Neck Cancer-Specific Survival Using Electronic Health Record Data: A Multisite Study.

In JCO clinical cancer informatics

PURPOSE : There is limited knowledge of the prediction of 2-year cancer-specific survival (CSS) in the head and neck cancer (HNC) population. The aim of this study is to develop and validate machine learning models and a nomogram for the prediction of 2-year CSS in patients with HNC using real-world data collected by major teaching and tertiary referral hospitals in New South Wales (NSW), Australia.

MATERIALS AND METHODS : Data collected in oncology information systems at multiple NSW Cancer Centres were extracted for 2,953 eligible adults diagnosed between 2000 and 2017 with squamous cell carcinoma of the head and neck. Death data were sourced from the National Death Index using record linkage. Machine learning and Cox regression/nomogram models were developed and internally validated in Python and R, respectively.

RESULTS : Machine learning models demonstrated highest performance (C-index) in the larynx and nasopharynx cohorts (0.82), followed by the oropharynx (0.79) and the hypopharynx and oral cavity cohorts (0.73). In the whole HNC population, C-indexes of 0.79 and 0.70 and Brier scores of 0.10 and 0.27 were reported for the machine learning and nomogram model, respectively. Cox regression analysis identified age, T and N classification, and time-corrected biologic equivalent dose in two gray fractions as independent prognostic factors for 2-year CSS. N classification was the most important feature used for prediction in the machine learning model followed by age.

CONCLUSION : Machine learning and nomogram analysis predicted 2-year CSS with high performance using routinely collected and complete clinical information extracted from oncology information systems. These models function as visual decision-making tools to guide radiotherapy treatment decisions and provide insight into the prediction of survival outcomes in patients with HNC.

Kotevski Damian P, Smee Robert I, Vajdic Claire M, Field Matthew

2023-Jan

General General

Strategies for the content determination of capsaicin and the identification of adulterated pepper powder using a hand-held near-infrared spectrometer.

In Food research international (Ottawa, Ont.)

To achieve the goals of rapid content determination of capsaicin and adulteration detection of pepper powder. The method based on the hand-held near-infrared spectrometer combined with ensemble preprocessing was proposed. DoE-based ensemble preprocessing technique was utilized to develop the partial least squares regression models of red pepper [Capsicum annuum L. var. conoides (Mill.) Irish] powders. The performance of final models was evaluated using coefficient of determination (R2), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD). Model development using selective ensemble preprocessing gave the best prediction of capsaicin in Yanjiao pepper powder (R2 = 0.9800, RPD = 7.090, RMSEP = 0.00689) and Tianying pepper powder (R2 = 0.8935, RPD = 3.017, RMSEP = 0.06154). Moreover, the potential of grey wolf optimizer-support vector machine (GWO-SVM) to detect adulterated pepper powder was investigated. The samples were composed of two authentic products and three different adulterants with different adulteration levels. The results showed that the classification accuracy of GWO-SVM model for Yanjiao peppers was over 90 %, which realized the adulteration detection of Yanjiao pepper. And GWO-SVM showed better performance in detecting adulterated Tianying pepper compared to hierarchical cluster analysis, orthogonal partial least squares discriminant analysis and random forest. In summary, the quality control strategy established in this paper can provide a solution for the adulteration detection and quality evaluation of pepper powder in a rapid and on-site way.

Wu Sijun, Wang Long, Zhou Guoming, Liu Chao, Ji Zhongrui, Li Zheng, Li Wenlong

2023-Jan

Adulteration, Capsaicin, Ensemble preprocessing, Grey wolf optimizer, Hand-held near-infrared spectrometer, Machine learning, Pepper powder