COVID Reviews
Published in

COVID Reviews

COVID-19 Analytics & AI Reviews

CDC illustration of SARS-CoV2: https://www.cdc.gov/media/subtopic/images.htm
https://www.cdc.gov/media/subtopic/images.htm

The goal of this publication is to provide a series of reviews related to recent analytics-focused articles on COVID-19. Our hope is to provide short summaries and critiques in a journal club-oriented format that can be quickly digested to assess the methodologies used, populations/outcomes assessed, impact of work, and strengths/weaknesses of each article. As the field continues to rapidly evolve with a likely reliance on pre-print articles, we hope to offer additional feedback to authors while further developing their work and future manuscripts along with a curated collection of articles for the research community.

We will publish our first two journal club reviews shortly on the following articles:

  1. Predicting Mortality Risk in Patients with COVID-19 Using Artificial Intelligence to Help Medical Decision-Making
  2. Towards an Artificial Intelligence Framework for Data-Driven Prediction of Coronavirus Clinical Severity

Please feel free to comment if you would like to contribute or if you have additional article recommendations for review.

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Wade Schulz, MD, PhD

Wade Schulz, MD, PhD

Dr. Schulz is an Assistant Professor of Laboratory Medicine and computational healthcare researcher at Yale School of Medicine