acePredicting 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…
The Elastic Net model distinguished three depression trajectories but remained a research prototype with no reported clinical deployment, prospective validation or impact on care.
Evidence
- Peer-reviewedInternational Psychogeriatrics2026-09-04
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Truvace Impact Record TRV-2026-0994, v2: “Predicting longitudinal depressive symptom trajectories among older Chinese adults with chronic health conditions: An interpretable machine learning study.” Truvace, 2026-10-26. /record/TRV-2026-0994 (accessed at citation time). sha256 2eca07d997425b42…
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