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Severity Prediction for COVID-19 Patients via Recurrent Neural Networks

Junghwan Lee1, Casey Ta1, Jae Hyun Kim1

  • 1Department of Biomedical Informatics, Columbia University, New York, N.Y.

AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science
|August 30, 2021
PubMed

Insights

This study developed a model using historical electronic health records (EHR) to predict severe COVID-19 outcomes. The model enables proactive risk management for patients upon hospital admission.

Area of Science:

  • Medical Informatics
  • Computational Biology
  • Epidemiology

Background:

  • The COVID-19 pandemic has caused significant global health challenges and strained healthcare systems.
  • Predicting severe outcomes in COVID-19 patients is crucial for resource allocation and timely intervention.

Purpose of the Study:

  • To develop a predictive model for severe COVID-19 outcomes using only pre-admission electronic health records (EHR).
  • To enable proactive risk stratification of COVID-19 patients at the time of hospital admission.

Main Methods:

  • Utilized recurrent neural networks (RNNs) to analyze historical EHR data.
  • Developed a model to predict the probability of a patient progressing to severe COVID-19 status (mechanical ventilation, tracheostomy, or death).

Main Results:

  • The model achieved an area under the receiver operating characteristic curve (AUC) of 0.846 in predicting patient outcomes.
  • The model demonstrated effectiveness using only historical EHR data, predating COVID-19 diagnosis.

Conclusions:

  • Historical EHR data can be effectively used to predict severe COVID-19 outcomes.
  • This approach facilitates proactive risk management, differentiating it from models requiring post-diagnosis data.

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