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Identifying Potential Factors Associated With Racial Disparities in COVID-19 Outcomes: Retrospective Cohort Study Using Machine Learning on Real-World Data

Osama Dasa, Chen Bai, Ruba Sajdeya, Stephen E Kimmel, Carl J Pepine, Matthew J Gurka J, Reinhard Laubenbacher, Thomas A Pearson, Mamoun T Mardini

JMIR Public Health Surveill 2024;10:e54421

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The Promising Success of Project Extension for Community Healthcare Outcomes (ECHO) Diabetes: Case Series

Lauren Figg, Ananta Addala, Ishaan Jain, Claudia Anez, Paul Midney, Corin DeChirico, Colleen Symanski, Brian C Fitzgerald, Kristi Colbert, Terry Raymer, Candy Stockton-Joreteg, Elizabeth Murphy, Leah Collins, Cyd Bernstein, Melanie Hechavarria, Eleni P Sheehan, Angelina Bernier, Sarah C Westen, Korey K Hood, Dessi P Zaharieva, Marina Basina, Nicolas Cuttriss, Stephanie L Filipp, Matthew J Gurka, Ashby F Walker, David M Maahs, Michael J Haller, Rayhan A Lal

JMIR Diabetes 2023;8:e46050

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Acceptability of an mHealth App for Monitoring Perinatal and Postpartum Mental Health: Qualitative Study With Women and Providers

Deepthi S Varma, Maya Mualem, Amie Goodin, Kelly K Gurka, Tony Soo-Tung Wen, Matthew J Gurka, Kay Roussos-Ross

JMIR Form Res 2023;7:e44500

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Noninvasive Diagnosis of Nonalcoholic Steatohepatitis and Advanced Liver Fibrosis Using Machine Learning Methods: Comparative Study With Existing Quantitative Risk Scores

Then, we calculated the specificity and sensitivity based on the Youden’s J statistic (Youden index) [21,22] determined from the ROC curve along with the AUC using the prediction from the 5-fold cross-validation. To reduce the bias of random grouping, for each machine learning method, we repeated the 5-fold cross-validation 20 times using different random seeds and calculated the mean specificity, mean sensitivity, mean AUC, and 95% CI.

Yonghui Wu, Xi Yang, Heather L Morris, Matthew J Gurka, Elizabeth A Shenkman, Kenneth Cusi, Fernando Bril, William T Donahoo