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This review aims to synthesize existing research on machine learning approaches for predicting circadian rhythm sleep-wake disorders.

Machine learning approaches in predicting circadian rhythm sleep-wake disorders: A review

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Systematic review evaluates machine learning for predicting circadian rhythm sleep-wake disorders, suggesting avenues for improvement.

This review aims to synthesize existing research on machine learning approaches for predicting circadian rhythm sleep-wake disorders.
Conducted a systematic search of multiple databases for studies on ML applications in circadian research.
Included studies focused on sighted adults and excluded those involving pediatric populations or neurodegenerative comorbidities.
Reviewed 22 eligible studies that utilized diverse data sources including sleep diaries and biological markers.
ML models showed promise for circadian phase estimation and CRSWD classification.
The evidence base was limited due to small sample sizes and methodological heterogeneity.
Recommendations include developing robust models with larger datasets and improved external validation strategies.

Sengupta et al. (2026) studied this question.

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