Abstract

Background: The use of artificial intelligence and machine learning (ML) tools is now common in the advancement of health care services and clinical risk estimation. Legacy systems make use of highly informative feature sets developed from years of clinical expertise and research to estimate different outcomes, but only recently have they been tested against novel statistical approaches. One such system, the Johns Hopkins Adjusted Clinical Group (ACG) System, is a long-standing and widely used approach to categorizing clinical risk factors, and it is amenable to ML techniques.

Objective: This study aims to test the ACG System using a contrasted area under the receiver operating characteristic (AUROC) and F1 classification optimization strategy and compare its performance against traditional logistic regression methods. Assuming that selected ML algorithms can be tuned to enhance overall measures of performance, this would strengthen arguments for incorporating them into ACG-related workflows.

 

 

Citation: 

Kitchen C, Zhang T, Lemke K, Pandya C, Kharrazi H, Weiner JP. Improving Models to Predict Care Utilization Using Machine Learning: Retrospective Observational Study. JMIR Form Res. 2026 Jun 26;10:e92820. doi: 10.2196/92820. PMID: 42361218; PMCID: PMC13308755.