A Long COVID Risk Predictor Focused on Clinical Workflow Integration
Bhattacharya, B.; DeLong, G.; Mitchell, E. G.; Munia, T. T. K.; Shetty, G.; Tariq, A.
Show abstract
For the NIH Long COVID Computational Challenge (L3C) in the Fall of 2022, we developed a machine learning model to predict who is at high risk for developing Long COVID, optimized for clinical deployment. Our submission won second prize in the competition. We present lessons learned, with details on the features, model selection and performance, fairness analysis, limitations, and deployment implications.
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