ArticleJournal of the American Medical Informatics Association : JAMIA2023
Comparison of time series clustering methods for identifying novel subphenotypes of patients with infection.
Article in Journal of the American Medical Informatics Association : JAMIA, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 24 citations in OpenAlex.
- Article
- Noradrenaline-trajectory phenotypes in septic shock: derivation and external validation in two independent cohorts.Intensive care medicine experimental · 2026Article
- Early heart-rate trajectory phenotypes predict short-term mortality in critically ill patients: a dynamic time-warping cluster analysis.Journal of clinical monitoring and computing · 2026Article
- Contrastive Transformer-Driven Discovery of Temporal Hemodynamic Subphenotypes in Cardiac Surgery Patients.medRxiv : the preprint server for health sciences · 2026Article
- Stratifying mortality risk in hypotension using outcome-oriented clustering of vital sign trajectories.iScience · 2026Article
- Data-driven spatial-temporal framework for exploring the heterogeneity and temporality of sepsis.Chinese medical journal · 2026Review
- A clustering-based trajectory analytics of functional loss and recovery among older adults.PloS one · 2026Article
- Identification of Clinical Subtypes of Surgical Sepsis in Critically Ill Patients Based on K-Means Clustering and Development of Parsimonious Classifier Model for High-Risk Subtype.Journal of inflammation research · 2026Article
- Group-based trajectory modeling of platelet dynamics in sepsis: from phenotypic identification to the exploration of prognostic mediators.Frontiers in medicine · 2026Article
- Trajectories of depressive symptoms in middle-aged and older Chinese adults: identifying subgroups, core symptoms and predictors.BMC psychiatry · 2025Observational
- Sepsis subphenotypes: bridging the gaps in sepsis treatment strategies.Frontiers in immunology · 2025Review
- Advanced biomarker clustering analysis reveals mortality predictors in burn patients with sepsis.Scientific reports · 2024Article
- The Precision Resuscitation With Crystalloids in Sepsis (PRECISE) Trial: A Trial Protocol.JAMA network open · 2024Article
- Novel cortisol trajectory sub-phenotypes in sepsis.Critical care (London, England) · 2024Article
- Exploring long-term breast cancer survivors' care trajectories using dynamic time warping-based unsupervised clustering.Journal of the American Medical Informatics Association : JAMIA · 2024Article
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Authors and funding
9 authors at 5 institutions in 1 country.
Funding
Abstract
objectiveSevere infection can lead to organ dysfunction and sepsis. Identifying subphenotypes of infected patients is essential for personalized management. It is unknown how different time series clustering algorithms compare in identifying these subphenotypes. MATERIALS AND
methodsPatients with suspected infection admitted between 2014 and 2019 to 4 hospitals in Emory healthcare were included, split into separate training and validation cohorts. Dynamic time warping (DTW) was applied to vital signs from the first 8 h of hospitalization, and hierarchical clustering (DTW-HC) and partition around medoids (DTW-PAM) were used to cluster patients into subphenotypes. DTW-HC, DTW-PAM, and a previously published group-based trajectory model (GBTM) were evaluated for agreement in subphenotype clusters, trajectory patterns, and subphenotype associations with clinical outcomes and treatment responses.
resultsThere were 12 473 patients in training and 8256 patients in validation cohorts. DTW-HC, DTW-PAM, and GBTM models resulted in 4 consistent vitals trajectory patterns with significant agreement in clustering (71-80% agreement, P < .001): group A was hyperthermic, tachycardic, tachypneic, and hypotensive. Group B was hyperthermic, tachycardic, tachypneic, and hypertensive. Groups C and D had lower temperatures, heart rates, and respiratory rates, with group C normotensive and group D hypotensive. Group A had higher odds ratio of 30-day inpatient mortality (P < .01) and group D had significant mortality benefit from balanced crystalloids compared to saline (P < .01) in all 3 models. DISCUSSION: DTW- and GBTM-based clustering algorithms applied to vital signs in infected patients identified consistent subphenotypes with distinct clinical outcomes and treatment responses.
conclusionTime series clustering with distinct computational approaches demonstrate similar performance and significant agreement in the resulting subphenotypes.
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