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Handbook of AI and Data Sciences for Sleep Disorders

Panos M. Pardalos (Hrsg.), Richard B. Berry (Hrsg.), Xiaochen Xian (Hrsg.)

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Springer Nature Switzerland img Link Publisher

Naturwissenschaften, Medizin, Informatik, Technik / Sonstiges

Beschreibung

The rise of lifestyle changes resulting from constant connectivity, irregular work schedules, heightened stress, and disruptive sleep patterns, have contributed to increasing insomnia rates.  Exacerbated by the COVID-19 pandemic, sleep disorders are more prevalent than ever. This handbook offers a comprehensive exploration of the fusion of Artificial Intelligence (AI) and data science within the realm of sleep disorders, presenting innovative approaches to diagnosis, treatment, and personalized care.

The interdisciplinary nature of this handbook fosters collaboration between experts from diverse fields, including computer science, engineering, neuroscience, medicine, public health, AI, data science, and sleep medicine. Each chapter delves into specific aspects of sleep disorder analysis, innovative methodologies, novel insights, and real-world applications that showcase the transformative potential of AI and data science in sleep medicine, from analyzing sleep patterns and predicting disorder risk factors to utilizing big data analytics for large-scale epidemiological studies. This handbook hopes to offer a comprehensive resource for researchers, clinicians, and policymakers striving to address the challenges in sleep medicine.

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Schlagwörter

nonlinear signal processing, spatiotemporal analysis, machine learning methods, pattern recognition sleep medicine, spatiotemporal analysis sleep medicine, data acquisition sleep medicine, time series analysis sleep medicine, sleep order case studies, anomaly detection sleep analysis, sleep disorders, optimization techniques sleep disorder detection, linear signal processing, sleep disorder analytics