Explainable machine learning model for assessing health status in patients with comorbid coronary heart disease and depression: Development and validation study.

Journal: International journal of medical informatics
PMID: 39874615

Abstract

BACKGROUND: Coronary heart disease (CHD) and depression frequently co-occur, significantly impacting patient outcomes. However, comprehensive health status assessment tools for this complex population are lacking. This study aimed to develop and validate an explainable machine learning model to evaluate overall health status in patients with comorbid CHD and depression.

Authors

  • Jiqing Li
    Department of Emergency Medicine Qilu Hospital of Shandong University Jinan China; Shandong Provincial Clinical Research Center for Emergency and Critical Care Medicine Institute of Emergency and Critical Care Medicine of Shandong University Chest Pain Center Qilu Hospital of Shandong University Jinan China; Key Laboratory of Emergency and Critical Care Medicine of Shandong Province Key Laboratory of Cardiopulmonary-Cerebral Resuscitation Research of Shandong Province Shandong Provincial Engineering Laboratory for Emergency and Critical Care Medicine Shandong Key Laboratory: Magnetic Field-free Medicine & Functional Imaging Qilu Hospital of Shandong University Jinan China.
  • Shuo Wu
    School of Chemistry, Dalian University of Technology, Dalian 116023, PR China. Electronic address: wushuo@dlut.edu.cn.
  • Jianhua Gu
    Office of National Central Cancer Registry, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

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