{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Liu J"],"funding":["Foundation of Hebei Provincial Department of Science and Technology"],"pagination":["10760296241304764"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11618897"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["30"],"pubmed_abstract":["<h4>Background</h4>Pulmonary embolism (PE) patients combined with heart failure (HF) have been reported to have a high short-term mortality. However, few studies have developed predictive tools of 30-day mortality for these patients in intensive care unit (ICU). This study aimed to construct and validate a machine learning (ML) model to predict 30-day mortality for PE patients combined with HF in ICU.<h4>Methods</h4>We enrolled patients with PE combined with HF in the Medical Information Mart for Intensive Care Database (MIMIC) and developed six ML models after feature selection. Further, eICU Collaborative Research Database (eICU-CRD) was utilized for external vali- dation. The area under curves (AUC), calibration curves, decision curve analysis (DCA), net reclassification improvement (NR"],"journal":["Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis"],"pubmed_title":["Interpretable Machine Learning Approach for Predicting 30-Day Mortality of Critical Ill Patients with Pulmonary Embolism and Heart Failure: A Retrospective Study."],"pmcid":["PMC11618897"],"funding_grant_id":["223777114D"],"pubmed_authors":["Liu J","Guo L","Guan Z","Li Z","Du S","He J","Yao T","Ma J","Li R","Liu G"],"additional_accession":[]},"is_claimable":false,"name":"Interpretable Machine Learning Approach for Predicting 30-Day Mortality of Critical Ill Patients with Pulmonary Embolism and Heart Failure: A Retrospective Study.","description":"<h4>Background</h4>Pulmonary embolism (PE) patients combined with heart failure (HF) have been reported to have a high short-term mortality. However, few studies have developed predictive tools of 30-day mortality for these patients in intensive care unit (ICU). This study aimed to construct and validate a machine learning (ML) model to predict 30-day mortality for PE patients combined with HF in ICU.<h4>Methods</h4>We enrolled patients with PE combined with HF in the Medical Information Mart for Intensive Care Database (MIMIC) and developed six ML models after feature selection. Further, eICU Collaborative Research Database (eICU-CRD) was utilized for external vali- dation. The area under curves (AUC), calibration curves, decision curve analysis (DCA), net reclassification improvement (NR","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Jan-Dec","modification":"2026-05-29T10:11:28.129Z","creation":"2025-04-04T01:23:26.198Z"},"accession":"S-EPMC11618897","cross_references":{"pubmed":["39633282"],"doi":["10.1177/10760296241304764"]}}