发表机构
Islamic University of Technology; Norwegian University of Science and Technology（伊斯兰科技大学; 挪威科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用可解释机器学习分析多中心临床队列，预测正念干预后抑郁结局，发现基线严重程度为最强预测因素，短期结局受临床背景影响，长期结局依赖行为依从性，支持情境感知的个性化心理健康支持。
AI 中文摘要
慢性或急性疾病患者的抑郁严重程度受到基线心理状态、人口统计学特征、临床背景及行为干预参与度之间复杂相互作用的影响。本文对一项多中心纵向临床队列进行了可解释的机器学习分析，以预测参与正念干预后第12周和第24周的贝克抑郁量表-II（BDI-II）评分。研究使用人口统计学变量、临床状况信息、医院中心标识符、基线BDI-II评分及治疗参与度指标来建模短期和长期抑郁结局。缺失的随访结局通过基于模型的随机插补程序处理，以在保持结局变异性的同时保留适度的样本量。研究评估了五种回归模型，涵盖正则化线性回归和基于树的集成方法。岭回归在12周预测中表现最佳，RMSE为5.186，R²为0.474；而LightGBM在24周预测中表现最佳，RMSE为5.038，R²为0.525。除预测准确性外，分析揭示了三个临床相关模式：基线严重程度始终是最强的总体预测因素，短期结局与临床和医院背景关联更强，长期结局则更依赖于行为依从性和人口统计学因素。疾病特异性和分层亚组分析进一步表明，预测因素在临床类别之间及内部存在显著差异。这些发现支持使用可解释的、情境感知的建模来为正念干预后的个性化心理健康支持提供信息。
英文摘要
Depression severity among patients with chronic or acute medical conditions is influenced by a complex interaction of baseline psychological state, demographic characteristics, clinical context, and engagement with behavioral interventions. This paper presents an interpretable machine-learning analysis of a multi-center longitudinal clinical cohort to predict Beck Depression Inventory-II (BDI-II) scores at 12 and 24 weeks following mindfulness-based intervention participation. The study uses demographic variables, clinical condition information, hospital-center identifiers, baseline BDI-II scores, and therapy engagement measures to model short-term and long-term depression outcomes. Missing follow-up outcomes were addressed using a model-based stochastic imputation procedure to preserve the modest sample size while maintaining outcome variability. Five regression models were evaluated, spanning regularized linear regression and tree-based ensemble methods. Ridge Regression achieved the best 12-week performance with an RMSE of 5.186 and R^2 of 0.474, while LightGBM achieved the best 24-week performance with an RMSE of 5.038 and R^2 of 0.525. Beyond prediction accuracy, the analysis reveals three clinically relevant patterns: baseline severity remains the strongest overall predictor, short-term outcomes are more strongly associated with clinical and hospital context, and long-term outcomes show greater dependence on behavioral adherence and demographic factors. Disease-specific and hierarchical subgroup analyses further indicate that predictors differ substantially across and within clinical categories. These findings support the use of interpretable, context-aware modeling to inform personalized mental-health support following mindfulness-based interventions.
