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TRV-2026-0994Certified recordPeer-reviewed

Predicting longitudinal depressive symptom trajectories among older Chinese adults with chronic health conditions: An interpretable machine learning study

Objective This study leveraged interpretable machine learning (ML) to map heterogeneous trajectories of depressive symptoms in Chinese older adults with chronic diseases, aiming to develop an interpretable, prediction-oriented framework for personalized mental health interventions. Methods We analyzed four-wave longitudinal data from 5492 participants in the China Health and Retirement Longitudinal Study. Following trajectory identification, 10 ML algorithms were compared. A 50-iteration bootstrap Recursive Feat…

Health · G Space — documented gain · certified 2026-09-06 · v1 · article view · machine-readable

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Elastic Net model distinguished three depressive symptom trajectories in older adults with chronic conditions and was deployed as an interactive web-based risk calculator for individualized risk profiling.

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Truvace Impact Record TRV-2026-0994, v1: “Predicting longitudinal depressive symptom trajectories among older Chinese adults with chronic health conditions: An interpretable machine learning study.” Truvace, 2026-09-06. /record/TRV-2026-0994 (accessed at citation time). sha256 0e0c5517d16d0e3b…

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