用可解释机器学习预测年轻人使用心理干预的参与度
EngageTriBoost: Predictive Modeling of User Engagement in Digital Mental Health Intervention Using Explainable Machine Learning
- 构建集成模型EngageTriBoost，分析用户行为预测参与度
- 最高达84%准确率，识别出情绪失调和污名感是关键影响因素
- 结果可解释，适合心理干预设计者与政策制定者参考
年轻成年人的心理健康问题日益严重，亟需数字心理健康干预（DMHI）等有效解决方案。尽管前景广阔，但DMHI面临初始使用率低和高流失率等采纳障碍。本研究利用机器学习分析针对风险大学生的eBridge DMHI用户行为数据，该平台通过动机访谈式在线咨询促进专业心理服务使用。提出的集成模型EngageTriBoost在预测用户参与度（签到与咨询师互动）方面达到最高84%的准确率。进一步采用SHAP分析揭示了情绪失调和感知污名等关键影响因素，提供了清晰可解释的洞察。研究证明可解释机器学习有助于深入理解用户参与机制，从而提升DMHI的采纳率与实际心理健康效果。
原文摘要 · Abstract (English)
Mental health challenges among young adults, are on the rise, necessitating effective solutions such as digital mental health interventions (DMHIs). Despite their promise, DMHIs face significant adoption barriers, including low initial uptake and high dropout rates. This study leverages machine learning (ML) to analyze behavioral patterns of users of a DMHI, eBridge, designed to increase the utilization of professional mental health services among at-risk college students through motivational interviewing-based online counseling. Our ensemble model, EngageTriBoost, achieved up to 84% accuracy in predicting engagement, measured by sign-ins and counselor interactions. We then applied the Shapley Additive exPlanations (SHAP) analysis which provided clear, interpretable insights into key factors influencing user engagement such as emotional dysregulation and perceived stigma, highlighting their critical effect on DMHI adoption. This study demonstrates the power of explainable ML for better understanding user engagement with DMHI to improve their adoption and achievable impact on mental health outcomes.
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