{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Gao H"],"funding":["the National Key Research and Development Program of China","Beijing Nova Program","the Key Research & Development Program of Guangxi","National Natural Science Foundation of China","Young Elite Scientists Sponsorship Program by CAST","the Joint Project on Regional High-Incidence Diseases Research of Guangxi Natural Science Foundation"],"pagination":["41581"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12644694"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["15(1)"],"pubmed_abstract":["This study aims to develop and validate a machine learning-based mortality risk prediction model for V-A ECMO patients to improve the precision of clinical decision-making. This multicenter retrospective cohort study included 280 patients receiving V-A ECMO from the Second Affiliated Hospital of Guangxi Medical University, Yulin First People's Hospital, and the MIMIC-IV database. The data from the Second Affiliated Hospital of Guangxi Medical University and the MIMIC-IV database were merged and randomly divided in a 7:3 ratio into a training set and an internal validation set, respectively. The dataset from Yulin First People's Hospital was reserved as an external validation cohort. The primary study outcome was defined as in-hospital mortality.Feature selection was conducted using Lasso r"],"journal":["Scientific reports"],"pubmed_title":["Development and validation of a machine learning model for predicting mortality risk in veno-arterial extracorporeal membrane oxygenation patients."],"pmcid":["PMC12644694"],"funding_grant_id":["GuiKeAB22080088","2022QNRC001","82360372","20220484053","2021YFC2701700","2023GXNSFDA026023"],"pubmed_authors":["Ling Y","Li S","Huang X","Mou C","Lu J","Chen Y","Gao H","Zhou K"],"additional_accession":[]},"is_claimable":false,"name":"Development and validation of a machine learning model for predicting mortality risk in veno-arterial extracorporeal membrane oxygenation patients.","description":"This study aims to develop and validate a machine learning-based mortality risk prediction model for V-A ECMO patients to improve the precision of clinical decision-making. This multicenter retrospective cohort study included 280 patients receiving V-A ECMO from the Second Affiliated Hospital of Guangxi Medical University, Yulin First People's Hospital, and the MIMIC-IV database. The data from the Second Affiliated Hospital of Guangxi Medical University and the MIMIC-IV database were merged and randomly divided in a 7:3 ratio into a training set and an internal validation set, respectively. The dataset from Yulin First People's Hospital was reserved as an external validation cohort. The primary study outcome was defined as in-hospital mortality.Feature selection was conducted using Lasso r","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Nov","modification":"2026-06-05T18:54:55.85Z","creation":"2026-05-20T03:14:45.275Z"},"accession":"S-EPMC12644694","cross_references":{"pubmed":["41286089"],"doi":["10.1038/s41598-025-25423-4"]}}