Work

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

Year: 2024

Type: article

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.

Source: Health Services and Outcomes Research Methodology

DOI: 10.1007/s10742-024-00324-7

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

Institutions: Wichita State University, University of Massachusetts Lowell, UMass Memorial Health Care, University of Massachusetts Chan Medical School, UMass Memorial Medical Center

Language: English

Topic: Machine Learning in Healthcare

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $3,690