Li Jiqing, Wu Shuo, Gu Jianhua
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.
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.
Int J Med Inform. 2025 Apr;196:105808. doi: 10.1016/j.ijmedinf.2025.105808. Epub 2025 Jan 23.
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.
Utilizing data from the 2021-2022 Behavioral Risk Factor Surveillance System, we developed and externally validated machine learning models to predict overall health status, defined as having both poor physical and mental health for ≥ 14 days in the past 30 days. Eleven machine learning algorithms were evaluated, including artificial neural networks, support vector machines, and ensemble methods. The SHapley Additive exPlanations (SHAP) method was employed to enhance model interpretability. Model performance was assessed using discrimination, calibration, and decision curve analysis.
The study included 9,747 participants in the derivation cohort and 8,394 in the external validation cohort. Among the eleven algorithms evaluated, an optimized XGBoost model with eight key features demonstrated balanced performance. SHAP analysis revealed that employment status, physical activity, income, and age were the most influential predictors. The model maintained good discrimination (AUC 0.712, 95% CI 0.703-0.721 in derivation; AUC 0.711, 95% CI 0.701-0.721 in validation), calibration and clinical utility across both cohorts.
Our explainable machine learning model provides a novel, comprehensive approach to assessing health status in patients with comorbid CHD and depression, offering valuable insights for personalized management strategies.
冠心病（CHD）与抑郁症常同时出现，对患者的预后产生重大影响。然而，针对这一复杂人群的综合健康状况评估工具却很缺乏。本研究旨在开发并验证一种可解释的机器学习模型，以评估合并冠心病和抑郁症患者的整体健康状况。
利用2021 - 2022年行为危险因素监测系统的数据，我们开发并外部验证了机器学习模型，以预测整体健康状况，定义为在过去30天内，身心健康状况不佳持续≥14天。评估了11种机器学习算法，包括人工神经网络、支持向量机和集成方法。采用SHapley加性解释（SHAP）方法来增强模型的可解释性。使用区分度、校准度和决策曲线分析来评估模型性能。
研究纳入了9747名推导队列参与者和8394名外部验证队列参与者。在评估的11种算法中，具有8个关键特征的优化XGBoost模型表现出平衡的性能。SHAP分析显示，就业状况、身体活动、收入和年龄是最具影响力的预测因素。该模型在两个队列中均保持了良好的区分度（推导队列中AUC为0.712，95%CI为0.703 - 0.721；验证队列中AUC为0.711，95%CI为0.701 - 0.721）、校准度和临床实用性。
我们的可解释机器学习模型为评估合并冠心病和抑郁症患者的健康状况提供了一种新颖、全面的方法，为个性化管理策略提供了有价值的见解。
