ArticlePloS one2023
Use of machine learning to identify risk factors for insomnia.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 2 of them syntheses that pooled it.
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Who cites it
42 citing papers in PubMed, 2 syntheses or guidelines pooled it, 50 citations in OpenAlex.
- Prevalence of metabolic syndrome in patients with inflammatory bowel disease: a meta-analysis on a global scale.Journal of health, population, and nutrition · 2025Pooled it
- Artificial Intelligence in Malnutrition: A Systematic Literature Review.Advances in nutrition (Bethesda, Md.) · 2024Pooled it
- Frailty-Driven Prediction of Inpatient Obstructive Sleep Apnea and Related Sleep Disorder Diagnoses Using Explainable AI.Biomedicines · 2026Article
- Revolutionizing Sleep Medicine: The Impact of Machine Learning on Diagnosis, Treatment, and Personalized Care.Health science reports · 2026Article
- Explainable machine learning for depression risk prediction in adults with obesity: development of an online tool.BMC medical informatics and decision making · 2026Article
- Identification and risk-factor analysis for individuals at high risk for keratoconus via machine learning and logistic regression.PloS one · 2026Article
- Development, validation, and visualization of a machine learning-based predictive model for depression risk in sleep disorder patients.BMC psychiatry · 2025Article
- Interpretable machine learning model for low bone density screening in older adults using demographic and anthropometric data: findings from 2005 to 2020 NHANES.BMC medical informatics and decision making · 2025Article
- The Visualization of the Importance of Covariance Importance in a Machine Learning Model for Advanced Liver Fibrosis in a Nationally Representative Sample.JGH open : an open access journal of gastroenterology and hepatology · 2025Article
- Insomnia in university students: a review of etiological factors and the advancement of data analytic techniques.Sleep & breathing = Schlaf & Atmung · 2025Review
- The application of suitable sports games for junior high school students based on deep learning and artificial intelligence.Scientific reports · 2025Article
- Negative association between body roundness index and constipation: insights from NHANES.Journal of health, population, and nutrition · 2025Article
- Association between red blood cell distribution width-to-albumin ratio and depression: a cross-sectional analysis among US adults, 2011-2018.BMC psychiatry · 2025Article
- Association of dietary calcium intake with chronic bronchitis and emphysema.Journal of health, population, and nutrition · 2025Article
- Network-based predictive models for artificial intelligence: an interpretable application of machine learning techniques in the assessment of depression in stroke patients.BMC geriatrics · 2025Article
- Association of dietary health indices with frailty.BMC public health · 2025Article
- Change in lifestyle and mental health in young adults: an exploratory study with hybrid machine learning.Frontiers in public health · 2025Article
- Prevalence and Factors Associated with Insomnia Among Chronic Disease Patients in Bangladesh: A Machine Learning Study.Nature and science of sleep · 2025Article
- Exploring predictors of insomnia severity in shift workers using machine learning model.Frontiers in public health · 2025Article
- A capsule network-based public health prediction system for chronic diseases: clinical and community implications.Frontiers in public health · 2025Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
importanceSleep is critical to a person's physical and mental health, but there are few studies systematically assessing risk factors for sleep disorders.
objectiveThe objective of this study was to identify risk factors for a sleep disorder through machine-learning and assess this methodology. DESIGN, SETTING, AND
participantsA retrospective, cross-sectional cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES) was conducted in patients who completed the demographic, dietary, exercise, and mental health questionnaire and had laboratory and physical exam data.
methodsA physician diagnosis of insomnia was the outcome of this study. Univariate logistic models, with insomnia as the outcome, were used to identify covariates that were associated with insomnia. Covariates that had a p<0.0001 on univariate analysis were included within the final machine-learning model. The machine learning model XGBoost was used due to its prevalence within the literature as well as its increased predictive accuracy in healthcare prediction. Model covariates were ranked according to the cover statistic to identify risk factors for insomnia. Shapely Additive Explanations (SHAP) were utilized to visualize the relationship between these potential risk factors and insomnia.
resultsOf the 7,929 patients that met the inclusion criteria in this study, 4,055 (51% were female, 3,874 (49%) were male. The mean age was 49.2 (SD = 18.4), with 2,885 (36%) White patients, 2,144 (27%) Black patients, 1,639 (21%) Hispanic patients, and 1,261 (16%) patients of another race. The machine learning model had 64 out of a total of 684 features that were found to be significant on univariate analysis (P<0.0001 used). These were fitted into the XGBoost model and an AUROC = 0.87, Sensitivity = 0.77, Specificity = 0.77 were observed. The top four highest ranked features by cover, a measure of the percentage contribution of the covariate to the overall model prediction, were the Patient Health Questionnaire depression survey (PHQ-9) (Cover = 31.1%), age (Cover = 7.54%), physician recommendation of exercise (Cover = 3.86%), weight (Cover = 2.99%), and waist circumference (Cover = 2.70%).
conclusionMachine learning models can effectively predict risk for a sleep disorder using demographic, laboratory, physical exam, and lifestyle covariates and identify key risk factors.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.