Learning Clinical Concepts for Predicting Risk of Progression to Severe COVID-19

• URL: http://arxiv.org/abs/2208.13126v1
• Date: Sun, 28 Aug 2022 02:59:35 GMT
• Title: Learning Clinical Concepts for Predicting Risk of Progression to Severe COVID-19
• Authors: Helen Zhou, Cheng Cheng, Kelly J. Shields, Gursimran Kochhar, Tariq Cheema, Zachary C. Lipton, Jeremy C. Weiss
• Abstract summary: Using data from a major healthcare provider, we develop survival models predicting severe COVID-19 progression. We develop two sets of high-performance risk scores: (i) an unconstrained model built from all available features; and (ii) a pipeline that learns a small set of clinical concepts before training a risk predictor.
• Score: 17.781861866125023
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With COVID-19 now pervasive, identification of high-risk individuals is crucial. Using data from a major healthcare provider in Southwestern Pennsylvania, we develop survival models predicting severe COVID-19 progression. In this endeavor, we face a tradeoff between more accurate models relying on many features and less accurate models relying on a few features aligned with clinician intuition. Complicating matters, many EHR features tend to be under-coded, degrading the accuracy of smaller models. In this study, we develop two sets of high-performance risk scores: (i) an unconstrained model built from all available features; and (ii) a pipeline that learns a small set of clinical concepts before training a risk predictor. Learned concepts boost performance over the corresponding features (C-index 0.858 vs. 0.844) and demonstrate improvements over (i) when evaluated out-of-sample (subsequent time periods). Our models outperform previous works (C-index 0.844-0.872 vs. 0.598-0.810).

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