[HTML][HTML] Use of machine learning to identify risk factors for insomnia

AA Huang, SY Huang - PloS one, 2023 - journals.plos.org
Importance Sleep is critical to a person's physical and mental health, but there are few
studies systematically assessing risk factors for sleep disorders. Objective The objective of …

Comparative study of various machine learning algorithms for prediction of Insomnia

R Ahuja, V Vivek, M Chandna, S Virmani… - Research Anthology on …, 2022 - igi-global.com
An early diagnosis of insomnia can prevent further medical aids such as anger issues, heart
diseases, anxiety, depression, and hypertension. Fifteen machine learning algorithms have …

Brief digital sleep questionnaire powered by machine learning prediction models identifies common sleep disorders

AR Schwartz, M Cohen-Zion, LV Pham, A Gal… - Sleep Medicine, 2020 - Elsevier
Introduction We developed and validated an abbreviated Digital Sleep Questionnaire (DSQ)
to identify common societal sleep disturbances including insomnia, delayed sleep phase …

Deep learning approaches for sleep disorder prediction in an asthma cohort

DV Phan, NP Yang, CY Kuo, CL Chan - Journal of Asthma, 2021 - Taylor & Francis
Objective Sleep is a natural activity of humans that affects physical and mental health;
therefore, sleep disturbance may lead to fatigue and lower productivity. This study examined …

[HTML][HTML] Application and interpretation of machine learning models in predicting the risk of severe obstructive sleep apnea in adults

Y Shi, Y Zhang, Z Cao, L Ma, Y Yuan, X Niu… - BMC Medical Informatics …, 2023 - Springer
Background Obstructive sleep apnea (OSA) is a globally prevalent disease with a complex
diagnostic method. Severe OSA is associated with multi-system dysfunction. We aimed to …

Objective relationship between sleep apnea and frequency of snoring assessed by machine learning

H Alshaer, R Hummel, M Mendelson… - Journal of Clinical …, 2019 - jcsm.aasm.org
Study Objectives: Snoring is perceived to be directly proportional to sleep apnea severity,
especially obstructive sleep apnea (OSA), but this notion has not been thoroughly and …

Predicting depressive symptoms in middle-aged and elderly adults using sleep data and clinical health markers: A machine learning approach

SRBS Gomes, M von Schantz, M Leocadio-Miguel - Sleep Medicine, 2023 - Elsevier
Objectives Comorbid depression is a highly prevalent and debilitating condition in middle-
aged and elderly adults, particularly when associated with obesity, diabetes, and sleep …

[HTML][HTML] Clinical applications of artificial intelligence in sleep medicine: a sleep clinician's perspective

A Bandyopadhyay, C Goldstein - Sleep and Breathing, 2023 - Springer
Background The past few years have seen a rapid emergence of artificial intelligence (AI)-
enabled technology in the field of sleep medicine. AI refers to the capability of computer …

[HTML][HTML] Predictive power of XGBoost_BiLSTM model: a machine-learning approach for accurate sleep apnea detection using electronic health data

A Javeed, JS Berglund, AL Dallora, MA Saleem… - International Journal of …, 2023 - Springer
Sleep apnea is a common disorder that can cause pauses in breathing and can last from a
few seconds to several minutes, as well as shallow breathing or complete cessation of …

[HTML][HTML] New approach for analyzing self-reporting of insomnia symptoms reveals a high rate of comorbid insomnia across a wide spectrum of chronic diseases

B Katic, J Heywood, F Turek, E Chiauzzi, TE Vaughan… - Sleep Medicine, 2015 - Elsevier
Background Insomnia is increasingly recognized to be comorbid with one or more medical
conditions. This study used an online research platform to characterize insomnia across …