Using Machine Learning to Discover Unexpected Patterns in Clinical Data: A Case Study in COVID-19 Sub-cohort Discovery

Abstract

Abstract As clinicians are faced with a deluge of new information, data science can play a key role in highlighting key features towards developing new clinical hypotheses. Indeed, insights derived from machine learning can serve as a clinical support tool by connecting care providers with results from big data analysis to identify latent patterns that may not be easily detected by even skilled human observers. In this work, we show an example of collaboration between clinicians and data scientists during the COVID-19 pandemic, identifying subgroups of COVID-19 patients with unanticipated outcomes or who are high-risk for severe disease or death. We apply a random forest classifier model to predict adverse patient outcomes early in the disease course, and we connect our classification results to unsupervised clustering of patient features that may underpin patient risk. The paradigm for using data science for hypothesis generation and clinical decision support, as well as our triage classification approach and unsupervised clustering methods to determine patient cohorts, are applicable to driving rapid hypothesis generation and iteration in a variety of clinical challenges, including future public health crises.

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