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Health Services and Outcomes Research Methodology

Open Access 18-03-2024 | COVID-19 | Case Study

A machine learning approach for diagnostic and prognostic predictions, key risk factors and interactions

Authors: Murtaza Nasir, Nichalin S. Summerfield, Stephanie Carreiro, Dan Berlowitz, Asil Oztekin

Published in: Health Services and Outcomes Research Methodology

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Abstract

Machine learning (ML) has the potential to revolutionize healthcare, allowing healthcare providers to improve patient-care planning, resource planning and utilization. Furthermore, identifying key-risk-factors and interaction-effects can help service-providers and decision-makers to institute better policies and procedures. This study used COVID-19 electronic health record (EHR) data to predict five crucial outcomes: positive-test, ventilation, death, hospitalization days, and ICU days. Our models achieved high accuracy and precision, with AUC values of 91.6%, 99.1%, and 97.5% for the first three outcomes, and MAE of 0.752 and 0.257 days for the last two outcomes. We also identified interaction effects, such as high bicarbonate in arterial blood being associated with longer hospitalization in middle-aged patients. Our models are embedded in a prototype of an online decision support tool that can be used by healthcare providers to make more informed decisions.
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Metadata
Title
A machine learning approach for diagnostic and prognostic predictions, key risk factors and interactions
Authors
Murtaza Nasir
Nichalin S. Summerfield
Stephanie Carreiro
Dan Berlowitz
Asil Oztekin
Publication date
18-03-2024
Publisher
Springer US
Keyword
COVID-19
Published in
Health Services and Outcomes Research Methodology
Print ISSN: 1387-3741
Electronic ISSN: 1572-9400
DOI
https://doi.org/10.1007/s10742-024-00324-7