Machine Learning for Depression Screening and Intervention: an Original Circadian Rhythm Score-based Methodology
Depression screening from large-scale behavioral data is challenged by fragmented circadian indicators, limited interpretability, and the lack of intervention-oriented analysis. 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 pres
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- LinkedLinked via arxiv author · 85%Bin Wang →
“Machine Learning for Depression Screening and Intervention: an Original Circadian Rhythm Score-based Methodology”
- LinkedLinked via arxiv author · 85%Shuo Lian →
“Machine Learning for Depression Screening and Intervention: an Original Circadian Rhythm Score-based Methodology”
- LinkedLinked via arxiv author · 85%Yuanyuan Hou →
“Machine Learning for Depression Screening and Intervention: an Original Circadian Rhythm Score-based Methodology”
- LinkedLinked via arxiv author · 85%Dexian Wang →
“Machine Learning for Depression Screening and Intervention: an Original Circadian Rhythm Score-based Methodology”
- LinkedLinked via arxiv author · 85%Peilan He →
“Machine Learning for Depression Screening and Intervention: an Original Circadian Rhythm Score-based Methodology”
- LinkedLinked via arxiv author · 85%Feng Hong →
“Machine Learning for Depression Screening and Intervention: an Original Circadian Rhythm Score-based Methodology”
- LinkedLinked via arxiv author · 85%Yanwei Yu →
“Machine Learning for Depression Screening and Intervention: an Original Circadian Rhythm Score-based Methodology”
- LinkedLinked via arxiv author · 85%Tianrui Li →
“Machine Learning for Depression Screening and Intervention: an Original Circadian Rhythm Score-based Methodology”