Automate Creating, Customizing, and Optimizing Comorbidity Indices Using a Data-Driven AI/ML Approach

Chih-Lin Chi; Yue Liang; Pui Ying Yew; Razan A El Khalifa; Bianca Shieu; Nai-Ching Chi

doi:10.3233/SHTI250955

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Automate Creating, Customizing, and Optimizing Comorbidity Indices Using a Data-Driven AI/ML Approach

Journal article

Open access

Automate Creating, Customizing, and Optimizing Comorbidity Indices Using a Data-Driven AI/ML Approach

Chih-Lin Chi, Yue Liang, Pui Ying Yew, Razan A El Khalifa, Bianca Shieu and Nai-Ching Chi

Studies in health technology and informatics, Vol.329, pp.825-829

08/07/2025

DOI: 10.3233/SHTI250955

PMID: 40775973

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Abstract

Due to individual differences in severity of illness, clinical studies typically use a comorbidity index to adjust outcomes. With the increasing use of electronic health records (EHRs) to assess the quality of care, a key question arises: how to adjust outcomes and control for severity effectively. Although one may adjust outcomes by using an existing comorbidity index, a suboptimal adjustment problem may occur when applying the existing comorbidity index to different outcomes or patient subgroups. Researchers can develop a new comorbidity index or modify an existing one to address this issue, but it requires time. This study proposes an Automatically Customized Comorbidity Index (ACCI) algorithm to automatically create, customize, and optimize a comorbidity index with EHR and a user's outcome of interest via prediction and optimization components. As an example, we use ACCI to create comorbidity indices for three outcomes of interest: statin-associated symptoms, statin therapy discontinuation, and statin days-supply. Here, we use random forest as the prediction and genetic algorithm as the optimization components. The result shows that ACCI iteratively improved the comorbidity index's prediction and relevance to the outcome. Those comorbidity indices also outperform the baselines, Charlson and Elixhauser comorbidity indices.

Algorithms

Comorbidity

Electronic Health Records

Artificial Intelligence

Humans

Hydroxymethylglutaryl-CoA Reductase Inhibitors - adverse effects

Hydroxymethylglutaryl-CoA Reductase Inhibitors - therapeutic use

Details

Title: Subtitle: Automate Creating, Customizing, and Optimizing Comorbidity Indices Using a Data-Driven AI/ML Approach
Creators: Chih-Lin Chi - University of Minnesota
Yue Liang - University of Minnesota
Pui Ying Yew - University of Minnesota
Razan A El Khalifa - University of Minnesota
Bianca Shieu - The University of Texas Health Science Center at San Antonio
Nai-Ching Chi - University of Iowa
Resource Type: Journal article
Publication Details: Studies in health technology and informatics, Vol.329, pp.825-829
DOI: 10.3233/SHTI250955
PMID: 40775973
NLM abbreviation: Stud Health Technol Inform
ISSN: 1879-8365
eISSN: 1879-8365
Publisher: IOS Press
Language: English
Date published: 08/07/2025
Academic Unit: Iowa Neuroscience Institute; Nursing
Record Identifier: 9984944723402771

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