Machine and Deep Learning in Molecular and Genetic Aspects of Sleep Research

An Artificial Intelligence Platform to Stratify the Risk of Experiencing Sleep Disturbance in University Students After Analyzing Psychological Health, Lifestyle, and Sports: A Multicenter Externally Validated Study

Background

Sleep problems are prevalent among university students, yet there is a lack of effective models to assess the risk of sleep disturbance. Artificial intelligence (AI) provides an opportunity to develop a platform for evaluating the risk. This study aims to develop and validate an AI platform to stratify the risk of experiencing sleep disturbance for university students.

Methods

A total of 2243 university students were included, with 1882 students from five universities comprising the model derivation group and 361 students from two additional universities forming the external validation group. Six machine learning techniques, including extreme gradient boosting machine (eXGBM), decision tree (DT), k-nearest neighbor (KNN), random forest (RF), neural network (NN), and support vector machine (SVM), were employed to train models using the same set of features. The models' prediction performance was assessed based on discrimination and calibration, and feature importance was determined using Shapley Additive exPlanations (SHAP) analysis.

Results

The prevalence of sleep disturbance was 44.69% in the model derivation group and 49.58% in the external validation group. Among the developed models, eXGBM exhibited superior performance, surpassing other models in metrics such as area under the curve (0.779, 95% CI: 0.728-0.830), accuracy (0.710), precision (0.737), F1 score (0.692), Brier score (0.193), and log loss (0.569). Calibration and decision curve analyses demonstrated favorable calibration ability and clinical net benefits, respectively. SHAP analysis identified five key features: stress score, severity of depression, vegetable consumption, age, and sedentary time. The AI platform was made available online at https://sleepdisturbancestudents-xakgzwectsw85cagdgkax9.streamlit.app/, enabling users to calculate individualized risk of sleep disturbance.

Conclusion

Sleep disturbance is prevalent among university students. This study presents an AI model capable of identifying students at high risk for sleep disturbance. The AI platform offers a valuable resource to guide interventions and improve sleep outcomes for university students.

Sleep Neurology's Toolkit at the Crossroads: Challenges and Opportunities in Neurotherapeutics Lost and Found in Translation

We find ourselves at our present crossroads with a well-traveled toolkit, perhaps too well worn but with aspirational hopes and dreams for the field of sleep neurotherapeutics. This volume is organized thematically into six topical domains that parallel the major subspecialty areas of contemporary clinical sleep neurology practice, as well as novel directions and opportunities. The issue begins with an overview of the central disorders of hypersomnolence, including narcolepsy, idiopathic hypersomnia and other hypersomnia disorders, and the related use of the entire broad range of stimulant and wake-promoting pharmacotherapies. Next, the range of behavioral therapies, application of light and light restriction and melatonin therapies, and hypnotic pharmacotherapies useful in insomnia and circadian sleep-wake rhythm disorders are reviewed, followed by an overview of treatment options for sleep-related breathing disorders including positive airway pressure and the novel approach of hypoglossal neurostimulation for obstructive sleep apnea. The parasomnias and sleep-related movement disorders, including NREM disorders of arousal, REM parasomnias (nightmares and isolated sleep paralysis and idiopathic/isolated REM sleep behavior disorder, and restless legs syndrome are then discussed, and the applications of sleep neurotherapeutics in sleep and neurological disease are reviewed, including neurodevelopmental, epileptic, autoimmune encephalopathies, and neurodegenerative diseases. Last, the novel directions and opportunities in sleep neurology offered by cannabinoid therapies and machine learning/artificial intelligence methodology conclude this comprehensive survey of contemporary sleep neurology. We hope that you find this volume to be a useful and inspirational support tool for the work that matters most, your care of all our sleep neurology patients in the clinics.