{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Hu C"],"funding":["Subject Cultivation Project of Zhongnan Hospital of Wuhan University","National Natural Science Foundation of China"],"pagination":["1949-1964"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9617989"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["11(5)"],"pubmed_abstract":["<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 betwee"],"journal":["Infectious diseases and therapy"],"pubmed_title":["Application of Machine Learning for Clinical Subphenotype Identification in Sepsis."],"pmcid":["PMC9617989"],"funding_grant_id":["ZNXKPY2021001","81772046","81971816"],"pubmed_authors":["Hu C","Li Y","Wang F","Peng Z"],"additional_accession":[]},"is_claimable":false,"name":"Application of Machine Learning for Clinical Subphenotype Identification in Sepsis.","description":"<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 betwee","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Oct","modification":"2025-04-25T20:51:34.9Z","creation":"2025-04-06T08:36:07.19Z"},"accession":"S-EPMC9617989","cross_references":{"pubmed":["36006560"],"doi":["10.1007/s40121-022-00684-y"]}}