ReviewFrontiers in artificial intelligence2022
Phenotype clustering in health care: A narrative review for clinicians.
Review in Frontiers in artificial intelligence, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
61 citing papers in PubMed.
- New insights on hidradenitis suppurativa phenotypes and treatment response: An exploratory automated analysis of the SUNSHINE and SUNRISE trials.Journal of the European Academy of Dermatology and Venereology : JEADV · 2025Trial
- Explainable SHAP-space fall risk profiling from electronic health records for inpatient prevention planning.npj health systems · 2026Article
- Clustering analysis of acute mountain sickness susceptibility among young adults during rapid ascent using low-altitude step test data.Physiological reports · 2026Article
- Phenotyping Preeclampsia Using Unsupervised Machine Learning: A Prospective Cohort Study.BJOG : an international journal of obstetrics and gynaecology · 2026Article
- Systematic review of artificial intelligence and digital-automated tools for screening, diagnosis and characterisation of primary ciliary dyskinesia.ERJ open research · 2026Article
- Heterogeneity of Treatment Effect of DOACs vs Warfarin in Atrial Fibrillation.JACC. Advances · 2026Article
- Phenotyping treatment-naive uncontrolled asthma in adults: A primary care framework.The journal of allergy and clinical immunology. Global · 2026Article
- Spectral clustering identifies patterns of chiropractic care in a national longitudinal cohort.JAMIA open · 2026Article
- Good things come in threes: evaluating clinical utility of machine learning-derived clusters.JAMIA open · 2026Review
- Long COVID longitudinal symptoms burden clusters within a national community-based cohort.BMC infectious diseases · 2026Article
- Identifying frailty trajectories in older patients with acute myocardial infarction using structural entropy clustering: a prospective cohort study.BMC geriatrics · 2026Article
- Influence of Phenotypes on Short-Term Outcomes in Hospitalized Heart Failure with Preserved Ejection Fraction-Insights from a North-Eastern Romanian Cohort.Medical sciences (Basel, Switzerland) · 2026Article
- Impact of Circulating Tumor DNA and Copy-Number Alterations on Clinical Outcome in Relapsed/Refractory Germ Cell Tumors Treated With Salvage High-Dose Chemotherapy.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2026Article
- Unsupervised cluster analysis identifies risk profiles driving heterogeneity and survival patterns in aortic aneurysm patients.Scientific reports · 2026Article
- Clustering of disease trajectories with explainable machine learning: A case study on postoperative delirium phenotypes.PLOS digital health · 2026Article
- Unsupervised clustering identifies distinct phenotypes in acute myocardial infarction: insights from the FAST-MI 2015 registry.Frontiers in artificial intelligence · 2026Article
- Clinical phenotypes and risk stratification for limb-threatening outcomes in acute compartment syndrome: a multicenter externally validated study.Frontiers in artificial intelligence · 2026Article
- Unsupervised machine learning reveals prognostic value of dynamic carbohydrate antigen 125 trajectory in gastric cancer.BMC cancer · 2025Article
- Machine learning identifies clusters of multimorbidity among decedents with inflammatory bowel disease.Communications medicine · 2025Article
- Identifying Stigma Phenotypes in Social Media Narratives of Substance Use: Observational Study.Journal of medical Internet research · 2025Observational
1 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors.
Funding
Abstract
Human pathophysiology is occasionally too complex for unaided hypothetical-deductive reasoning and the isolated application of additive or linear statistical methods. Clustering algorithms use input data patterns and distributions to form groups of similar patients or diseases that share distinct properties. Although clinicians frequently perform tasks that may be enhanced by clustering, few receive formal training and clinician-centered literature in clustering is sparse. To add value to clinical care and research, optimal clustering practices require a thorough understanding of how to process and optimize data, select features, weigh strengths and weaknesses of different clustering methods, select the optimal clustering method, and apply clustering methods to solve problems. These concepts and our suggestions for implementing them are described in this narrative review of published literature. All clustering methods share the weakness of finding potential clusters even when natural clusters do not exist, underscoring the importance of applying data-driven techniques as well as clinical and statistical expertise to clustering analyses. When applied properly, patient and disease phenotype clustering can reveal obscured associations that can help clinicians understand disease pathophysiology, predict treatment response, and identify patients for clinical trial enrollment.
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Registered trials
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.