Beyond Baseline Severity: Temporal and Disease-Specific Predictors of Depression Outcomes Following Mindfulness Interventions

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.

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、临床背景以及行为干预参与度之间复杂相互作用的影响。

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.

本文提出了一种针对多中心纵向临床队列的可解释机器学习分析，旨在预测参与正念干预后 12 周和 24 周的贝克抑郁量表第二版（BDI-II）评分。

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.

该研究利用人口统计学变量、临床状况信息、医院中心标识符、基线 BDI-II 评分以及治疗参与度指标，对短期和长期的抑郁预后进行建模。

Missing follow-up outcomes were addressed using a model-based stochastic imputation procedure to preserve the modest sample size while maintaining outcome variability.

研究通过基于模型的随机插补程序处理了缺失的随访结果，以在保持结果变异性的同时保留适度的样本量。

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.

研究评估了五种回归模型，涵盖了正则化线性回归和基于树的集成方法。岭回归（Ridge Regression）在 12 周时表现最佳，RMSE 为 5.186，R^2 为 0.474；而 LightGBM 在 24 周时表现最佳，RMSE 为 5.038，R^2 为 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.

除了预测准确性外，该分析还揭示了三种具有临床意义的模式：基线严重程度仍然是整体最强的预测因子，短期预后与临床和医院背景的关联更为紧密，而长期预后则表现出对行为依从性和人口统计学因素的更大依赖。

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.

针对特定疾病的分层亚组分析进一步表明，预测因子在不同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、具有情境感知能力的建模方法，为正念干预后的个性化心理健康支持提供参考。