ArticleJournal of International Society of Preventive & Community Dentistry
Unsupervised Machine Learning Identified Distinct Population Clusters Based on Symptoms of Oral Pain, Psychological Distress, and Sleep Problems.
Article in Journal of International Society of Preventive & Community Dentistry. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 6 citations in OpenAlex.
- Temporomandibular Disorder-like Pain in Parkinson's Disease Is Associated with Motor Symptom Severity and Disability Levels.Journal of clinical medicine · 2026Article
- Response to venom immunotherapy: Exploratory retrospective machine learning clustering analysis.The journal of allergy and clinical immunology. Global · 2026Article
- Oral pain and comorbidities in an edentulous older population: A k-prototypes cluster analysis.PloS one · 2025Article
- A scoping review of the clinical application of machine learning in data-driven population segmentation analysis.Journal of the American Medical Informatics Association : JAMIA · 2023Article
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1 author at 1 institution in 1 country.
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Abstract
objectivesThe aims of this study were to explore the use of unsupervised machine learning in clustering the population based on reports of oral pain, psychological distress, and sleep problems and to compare demographic and socio-economic characteristics as well as levels of functional domains (work, social, and leisure) between clusters. MATERIALS AND
methodsIn this cross-sectional study, a total of 1613 participants from the National Health and Nutrition Examination Survey in 2017-2018 were analyzed. Five variables, including oral pain, depression, anxiety, sleep apnea, and excessive daytime sleepiness, were selected for cluster analysis using the
resultsFive distinct clusters were identified: healthy, norm, anxiety, apnea-comorbid, and pain-comorbid. The apnea-comorbid cluster had mean age of 59 years and higher proportion of men. The pain-comorbid cluster had mean age of 56 years and higher proportion of women. Whites constituted a majority of both comorbid clusters. The pain-comorbid cluster demonstrated the least percentage of individuals with college degree, the lowest income, and significant impairment in all functional domains.
conclusionThrough the use of unsupervised machine learning, the clusters with comorbidity of oral pain, psychological distress, and sleep problems have emerged. Major characteristics of the comorbid clusters included mean age below 60 years, White, and low levels of education and income. Functional domains were significantly impaired. The comorbid clusters thus call for public health intervention.
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