biostudies-literatureHu CSubject Cultivation Project of Zhongnan Hospital of Wuhan UniversityNational Natural Science Foundation of China1949-1964https://www.ebi.ac.uk/biostudies/studies/S-EPMC9617989biostudies-literatureUnknown11(5)<h4>Introduction</h4>Sepsis 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.<h4>Methods</h4>This 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 betweeInfectious diseases and therapyApplication of Machine Learning for Clinical Subphenotype Identification in Sepsis.PMC9617989ZNXKPY20210018177204681971816Hu CLi YWang FPeng ZfalseApplication of Machine Learning for Clinical Subphenotype Identification in Sepsis.<h4>Introduction</h4>Sepsis 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.<h4>Methods</h4>This 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 betwee2022-01-01T00:00:00Z2022 Oct2025-04-25T20:51:34.9Z2025-04-06T08:36:07.19ZS-EPMC96179893600656010.1007/s40121-022-00684-y