ArticleScientific reports2021
Combined unsupervised-supervised machine learning for phenotyping complex diseases with its application to obstructive sleep apnea.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 42 citations in OpenAlex.
- A foundation model for sleep-based risk stratification and clinical outcomes.Nature communications · 2026Article
- Bridging Data, Semantics, and Clinical Reasoning: A Knowledge Graph Framework for Pediatric Obstructive Sleep Apnea.Children (Basel, Switzerland) · 2026Article
- "Phenotyping obstructive sleep apnea: a pathway to precision medicine".Sleep & breathing = Schlaf & Atmung · 2026Review
- Redefining Obstructive Sleep Apnea: Multidimensional Phenotyping Beyond the Apnea-Hypopnea Index.Pathophysiology : the official journal of the International Society for Pathophysiology · 2026Review
- Unveiling Phenotypic Heterogeneity in Coronary Spastic Angina Through Multidimensional Risk Profiling: The FUJI-SPASM Study.JACC. Advances · 2026Article
- Clinical phenotypes and risk stratification for limb-threatening outcomes in acute compartment syndrome: a multicenter externally validated study.Frontiers in artificial intelligence · 2026Article
- Metabolic dysfunction-associated steatotic liver disease as a systemic disorder: extrahepatic manifestations and the need for data-driven phenotyping.Frontiers in endocrinology · 2026Review
- Machine learning identifies clusters of multimorbidity among decedents with inflammatory bowel disease.Communications medicine · 2025Article
- Article
- A comparative analysis of unsupervised machine-learning methods in PSG-related phenotyping.Journal of sleep research · 2025Article
- Unsupervised clustering for sepsis identification in large-scale patient data: a model development and validation study.Intensive care medicine experimental · 2025Article
- An Artificial Intelligence-Based Non-Invasive Approach for Cardiovascular Disease Risk Stratification in Obstructive Sleep Apnea Patients: A Narrative Review.Reviews in cardiovascular medicine · 2024Review
- [Sleep Innovation].Open respiratory archives · 2024Review
- Cardiac ultrasomics for acute myocardial infarction risk stratification and prediction of all-cause mortality: a feasibility study.Echo research and practice · 2024Article
- Identifying diseases symptoms and general rules using supervised and unsupervised machine learning.Scientific reports · 2024Article
- Paediatric sleep diagnostics in the 21st century: the era of "sleep-omics"?European respiratory review : an official journal of the European Respiratory Society · 2024Review
- Predicting the Risk of Sleep Disorders Using a Machine Learning-Based Simple Questionnaire: Development and Validation Study.Journal of medical Internet research · 2023Article
- Association of Polysomnographic Sleep Parameters With Neuroimaging Biomarkers of Cerebrovascular Disease in Older Adults With Sleep Apnea.Neurology · 2023Article
- Using Cluster Analysis to Overcome the Limits of Traditional Phenotype-Genotype Correlations: The Example ofGenes · 2023Article
- Unveiling diagnostic and therapeutic strategies for cervical cancer: biomarker discovery through proteomics approaches and exploring the role of cervical cancer stem cells.Frontiers in oncology · 2023Review
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Unsupervised clustering models have been widely used for multimetric phenotyping of complex and heterogeneous diseases such as diabetes and obstructive sleep apnea (OSA) to more precisely characterize the disease beyond simplistic conventional diagnosis standards. However, the number of clusters and key phenotypic features have been subjectively selected, reducing the reliability of the phenotyping results. Here, to minimize such subjective decisions for highly confident phenotyping, we develop a multimetric phenotyping framework by combining supervised and unsupervised machine learning. This clusters 2277 OSA patients to six phenotypes based on their multidimensional polysomnography (PSG) data. Importantly, these new phenotypes show statistically different comorbidity development for OSA-related cardio-neuro-metabolic diseases, unlike the conventional single-metric apnea-hypopnea index-based phenotypes. Furthermore, the key features of highly comorbid phenotypes were identified through supervised learning rather than subjective choice. These results can also be used to automatically phenotype new patients and predict their comorbidity risks solely based on their PSG data. The phenotyping framework based on the combination of unsupervised and supervised machine learning methods can also be applied to other complex, heterogeneous diseases for phenotyping patients and identifying important features for high-risk phenotypes.
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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.