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Existing approaches typically analyze sleep, activity, and social behaviors in isolation, failing to capture their joint circadian structure. To address this limitation, we first propose the Circadian Rhythm Score (CRS), a composite index that compresses multi-domain daily behaviors into a unified representation of circadian rhythm. CRS is constructed to maximize discriminative power for depression screening while preserving behavioral semantics through non-negativity constraints. Empirical results demonstrate near-lossless compression, where a single CRS retains almost the full predictive capability compared with multiple raw behavioral indicators. Building upon CRS, we develop an interpretable depression screening framework based on gradient-boosted trees and SHAP analysis, revealing nonlinear and saturation-like associations between circadian rhythm and depression risk. Beyond risk prediction, we further integrate interaction modeling and counterfactual regression to estimate heterogeneous and dose-dependent behavioral effects, enabling  \n∗ Bin Wang and Shuo Lian are co-first authors with equal contributions.†Feng Hong and Yanwei Yu are corresponding authors.  \nThis work is licensed under a Creative Commons Attribution 4 .0 International License. KDD’26, Jeju Island, Republic of Korea  \n© 2026 Copyright held by the owner/author(s) .  \nACM ISBN 979-8-4007-2259-2/2026/08  \n[https://doi.org/10.1145/3770855.3818913](https://doi.org/10.1145/3770855.3818913)  \nintervention-oriented reasoning under different circadian contexts. Experiments on the China Health and Retirement Longitudinal Study (CHARLS, n=15,233), demonstrate robust screening performance (ROC-AUC=0.825) and identify actionable behavioral thresholds, including a minimum effective exercise dose of approximately 300 MET-min/week and an optimal restorative nap duration of approximately 65 minutes for sleep-deprived individuals. By bridging supervised representation learning and interpretable modeling, this work provides a scalable framework for depression screening and intervention-aware healthcare data mining.  \nCCS Concepts  \n• Applied computing → Health informatics; • Computing methodologies → Modeling methodologies.  \nKeywords  \nAI for Healthcare, Depression Screening, Circadian Rhythm, Intervention Analysis, CHARLS  \nACM Reference Format:  \nBin Wang, Shuo Lian, Yuanyuan Hou, Dexian Wang, Peilan He, Feng Hong, Yanwei Yu, and Tianrui Li. ","cbCaieNtbf86HMJg","https://ap.wps.com/l/cbCaieNtbf86HMJg","pdf",4166381,12,"English","# Introduction\n## Challenges\n## Motivation","[{\"question\":\"What problem does the paper address in depression screening from behavioral data?\",\"answer\":\"It addresses the difficulty of using fragmented circadian indicators, limited interpretability, and the lack of intervention-oriented reasoning. Existing approaches also fail to capture the joint circadian structure across sleep, activity, and social behaviors.\"},{\"question\":\"What is the Circadian Rhythm Score (CRS) and why is it proposed?\",\"answer\":\"CRS is a composite index that compresses multi-domain daily behaviors into a unified representation of circadian rhythm. It is designed to maximize discriminative power for screening while preserving behavioral semantics through non-negativity constraints.\"},{\"question\":\"How does the work interpret the relationship between circadian rhythm and depression risk?\",\"answer\":\"It builds an interpretable screening framework using gradient-boosted trees and SHAP analysis. The results reveal nonlinear, saturation-like associations between circadian rhythm and depression risk.\"}]","Machine Learning for Depression Screening and Intervention - an Original Circadian Rhythm Score-based Methodology | PDF",1789978449]