arXiv：cs.LG· Muhammad Jawad Chowdhury, Sultanus Salehin, Akib Jayed Islam·· 4 小时前AI 评分26
正念干预后抑郁结局的时间与疾病特异性预测：一项可解释机器学习分析
Beyond Baseline Severity: Temporal and Disease-Specific Predictors of Depression Outcomes Following Mindfulness Interventions
AI 导读
一项多中心纵向临床队列研究用可解释机器学习预测正念干预后12周和24周的BDI-II抑郁评分。Ridge Regression在12周表现最佳（RMSE 5.186，R² 0.474），LightGBM在24周最佳（RMSE 5.038，R² 0.525）。分析显示基线严重程度是最强预测因子，短期结局更依赖临床和医院背景，长期结局更依赖行为依从性和人口学因素，且预测因子在不同临床类别间差异显著。
正文
Abstract: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.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.08809 [cs.LG] |
| (or arXiv:2610.08809v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.08809 arXiv-issued DOI via DataCite |
Submission history
From: Muhammad Jawad Chowdhury [view email]
[v1]
Sat, 19 Sep 2026 20:35:44 UTC (656 KB)
来源：arXiv：cs.LG · arxiv.org