ArticleInfectious diseases and therapy2022
Application of Machine Learning for Clinical Subphenotype Identification in Sepsis.
Article in Infectious diseases and therapy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Construction and validation of a machine learning-based prediction model for in-hospital acute kidney injury in patients with lung cancer complicated with sepsis: clinical and nursing applications.Journal of thoracic disease · 2026Article
- Contrastive Transformer-Driven Discovery of Temporal Hemodynamic Subphenotypes in Cardiac Surgery Patients.medRxiv : the preprint server for health sciences · 2026Article
- Glycemic Variability as a Predictor of Mortality in Sepsis Patients With Concurrent Persistent Inflammation, Immunosuppression, and Catabolism Syndrome.Immunity, inflammation and disease · 2026Article
- Artificial Intelligence Drives Advances in Multi-Omics Analysis and Precision Medicine for Sepsis.Biomedicines · 2026Review
- Artificial Intelligence- and Machine Learning-Assisted Subphenotyping for Personalized Immunotherapy in Sepsis.Journal of personalized medicine · 2026Review
- Digital convergence in pediatric sepsis: augmenting translation through AI and digital twins.International journal of surgery (London, England) · 2026Article
- The Impact of White Blood Cell Count Trajectories on Prognosis and Secondary Acute Kidney Injury in Sepsis Patients.Mediators of inflammation · 2026Article
- Exploring Novel Data-Driven Clustering Methods for Uncovering Patterns in Longitudinal Neonatal Postoperative Temperature Measurements.Mayo Clinic proceedings. Digital health · 2025Article
- Deriving consensus sepsis clusters via goal-directed subgroup identification in multi-omics study.Nature communications · 2025Article
- Development and validation of machine learning models to predict in-hospital mortality in ICU patients with sepsis and chronic kidney disease.BMC infectious diseases · 2025Article
- The 15th Anniversary ofLife (Basel, Switzerland) · 2025Review
- Unsupervised Machine Learning in Identification of Septic Shock Phenotypes and Their In-Hospital Outcomes: A Multicenter Cohort Study.Journal of clinical medicine · 2025Article
- Comparison of different AI systems for diagnosing sepsis, septic shock, and cardiogenic shock: a retrospective study.Scientific reports · 2025Article
- Deciphering the impact of sepsis phenotypes on improving clinical outcome predictions: a multicenter retrospective analysis based on critical care in China.Scientific reports · 2025Article
- Early sepsis mortality prediction model based on interpretable machine learning approach: development and validation study.Internal and emergency medicine · 2025Article
- SepsisJournal of medical microbiology · 2025Review
- Machine learning derivation of two cardiac arrest subphenotypes with distinct responses to treatment.Journal of translational medicine · 2025Article
- Research Progress of Biomarkers for Sepsis and Precision Medicine.Emergency medicine international · 2025Review
- Advancing specificity in delirium: The delirium subtyping initiative.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Joint Modeling of Social Determinants and Clinical Factors to Define Subphenotypes in Out-of-Hospital Cardiac Arrest Survival: Cluster Analysis.JMIR aging · 2023Article
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Abstract
introductionSepsis is a heterogeneous clinical syndrome. Identification of sepsis subphenotypes could lead to allowing more precise therapy. However, there is a lack of models to identify the subphenotypes in such patients. Thus, we aimed to identify possible subphenotypes and compare the clinical outcomes for subphenotypes in a large sepsis cohort.
methodsThis machine learning-based, cluster analysis was performed using the Medical Information Mart in Intensive Care (MIMIC)-IV database. We enrolled all adult (> 18 years old) patients diagnosed with sepsis in the first 24 h after intensive care unit (ICU) admission. K-means cluster analysis was performed to identify the number of classes. Multivariable logistic regression models were used to estimate the association between sepsis subphenotypes and in-hospital mortality.
resultsA total of 8817 participants with sepsis were enrolled. The median age was 66.8 (IQR, 55.9-77.1) years, and 38.1% (3361/8817) were female. Two subphenotypes resulted in optimal separation including 11 routinely available clinical variables obtained during the first 24 h after ICU admission. Participants in subphenotype B showed higher levels of lactate, glucose and creatinine, white blood cell count, sodium and heart rate and lower body temperature, platelet count, systolic blood pressure, hemoglobin and PaO
conclusionsTwo sepsis subphenotypes with different clinical outcomes could be rapidly identified using the K-means clustering analysis based on routinely available clinical data. This finding may help clinicians to identify the subphenotype rapidly at the bedside.
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