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

arXiv cs.LG Papers
machine-learning mental-health depression-prediction clinical-cohort interpretable-ai time-series

Summary

本文提出一种可解释的机器学习分析，利用多中心纵向临床队列预测参与正念干预后患者在12周和24周的BDI-II抑郁评分，发现基线严重程度仍是最强预测因子，而短期结局更依赖临床与医院背景，长期结局则更依赖行为依从性和人口统计因素。

arXiv:2610.08809v1 Announce Type: new 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.
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# Beyond Baseline Severity: Temporal and Disease-Specific Predictors of Depression Outcomes Following Mindfulness Interventions
Source: [https://arxiv.org/abs/2610.08809](https://arxiv.org/abs/2610.08809)
[View PDF](https://arxiv.org/pdf/2610.08809)[HTML \(experimental\)](https://arxiv.org/html/2610.08809v1)

> 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\.

## Submission history

From: Muhammad Jawad Chowdhury \[[view email](https://arxiv.org/show-email/e87608e7/2610.08809)\] **\[v1\]**Sat, 19 Sep 2026 20:35:44 UTC \(656 KB\)

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