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

Chih-Lin Chi1,2, Yue Liang1, Pui Ying Yew1

  • 1Institute for Health Informatics, University of Minnesota, USA.

Studies in Health Technology and Informatics
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Summary
This summary is machine-generated.

This study introduces an Automatically Customized Comorbidity Index (ACCI) algorithm to improve outcome adjustment in electronic health records (EHRs). ACCI optimizes comorbidity indices, outperforming existing methods for better clinical outcome assessment.

Keywords:
Customized comorbidity indexEHRartificial intelligence/machine learningoptimization and predictionstatin therapy

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Area of Science:

  • Health Informatics
  • Clinical Epidemiology
  • Biostatistics

Background:

  • Clinical studies adjust for illness severity using comorbidity indices.
  • Electronic health records (EHRs) necessitate effective outcome adjustment and severity control.
  • Existing comorbidity indices may be suboptimal for diverse outcomes or patient subgroups.

Purpose of the Study:

  • To propose an Automatically Customized Comorbidity Index (ACCI) algorithm.
  • To automatically create, customize, and optimize comorbidity indices using EHR data.
  • To enhance the adjustment of clinical outcomes based on patient severity.

Main Methods:

  • Developed the ACCI algorithm with prediction and optimization components.
  • Utilized random forest for prediction and genetic algorithm for optimization.
  • Applied ACCI to create comorbidity indices for statin-associated symptoms, therapy discontinuation, and days-supply.

Main Results:

  • ACCI iteratively improved comorbidity index prediction and outcome relevance.
  • The customized comorbidity indices generated by ACCI outperformed baseline indices (Charlson, Elixhauser).
  • Demonstrated ACCI's effectiveness in creating tailored indices for specific clinical outcomes.

Conclusions:

  • ACCI offers an automated and effective approach to developing customized comorbidity indices.
  • This method enhances the accuracy of outcome adjustment in EHR-based studies.
  • ACCI provides a valuable tool for improving the assessment of care quality and clinical research.