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

Journal: Health services & outcomes research methodology
Published Date: Mar 18, 2024

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 middleaged 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.

Authors

  • Murtaza Nasir
    Finance, Real Estate, & Decision Science Department, Barton School of Business, Wichita State University, Wichita, KS 67260, USA.
  • Nichalin S Summerfield
    Operations & Information Systems Department, Manning School of Business, University of Massachusetts Lowell, Lowell, MA 01854, USA.
  • Stephanie Carreiro
    Department of Emergency Medicine, University of Massachusetts Medical School & UMass Memorial Healthcare, Worcester, MA 01655, USA.
  • Dan Berlowitz
    Department of Public Health, Zuckerberg College of Health Sciences, University of Massachusetts Lowell, Lowell, MA 01854, USA.
  • Asil Oztekin
    Operations & Information Systems Department, Manning School of Business, University of Massachusetts Lowell, Lowell, MA 01854, USA.

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