{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Ni P"],"funding":["Science and Technology Development Project of Hangzhou","Key Program Cosponsored by Zhejiang Province and National Health Commission of China","Construction Fund of Medical Key Disciplines of Hangzhou"],"pagination":["8691"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11907063"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["15(1)"],"pubmed_abstract":["Cardiac arrest (CA) poses a significant global health challenge and often results in poor prognosis. We developed an interpretable and applicable machine learning (ML) model for predicting in-hospital mortality of CA patients who survived more than 72 h. A total of 721 patients were extracted from the Medical Information Mart for Intensive Care IV database, divided into the training set (n = 576) and the internal validation set (n = 145). The external validation set containing 856 cases were collected from four tertiary hospitals in Zhejiang Province. The primary outcome was in-hospital mortality. Eleven ML algorithms were utilized to establish prediction models based on data from 72 h after return of spontaneous circulation (ROSC). The results indicate that the CatBoost model exhibited th"],"journal":["Scientific reports"],"pubmed_title":["Development and validation of machine learning-based prediction model for outcome of cardiac arrest in intensive care units."],"pmcid":["PMC11907063"],"funding_grant_id":["202204A10","OO20200485","WKJ-ZJ-2315"],"pubmed_authors":["Zhang G","Zhang W","Zhu Y","Hu W","Zhang H","Zhang S","Ni P","Diao M"],"additional_accession":[]},"is_claimable":false,"name":"Development and validation of machine learning-based prediction model for outcome of cardiac arrest in intensive care units.","description":"Cardiac arrest (CA) poses a significant global health challenge and often results in poor prognosis. We developed an interpretable and applicable machine learning (ML) model for predicting in-hospital mortality of CA patients who survived more than 72 h. A total of 721 patients were extracted from the Medical Information Mart for Intensive Care IV database, divided into the training set (n = 576) and the internal validation set (n = 145). The external validation set containing 856 cases were collected from four tertiary hospitals in Zhejiang Province. The primary outcome was in-hospital mortality. Eleven ML algorithms were utilized to establish prediction models based on data from 72 h after return of spontaneous circulation (ROSC). The results indicate that the CatBoost model exhibited th","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Mar","modification":"2025-04-05T22:07:58.595Z","creation":"2025-04-05T22:07:58.595Z"},"accession":"S-EPMC11907063","cross_references":{"pubmed":["40082569"],"doi":["10.1038/s41598-025-93182-3"]}}