{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"submitter":["Jabal MS"],"pubmed_abstract":["PurposeAneurysmal rupture and subarachnoid hemorrhage (SAH) have an exceptionally high mortality and morbidity burden. The aim of this study was to develop interpretable machine learning models for predicting short-term poor outcomes defined by the National Inpatient Sample Subarachnoid Hemorrhage Outcome Measure (NIS-SOM).MethodsThe National Inpatient Sample (NIS) database was queried from 2008 to 2018 to identify patients diagnosed with SAH who had undergone endovascular coiling or clipping for intracranial aneurysm. Demographic, comorbidity, risk factor, and hospital characteristic variables were recorded. Variables were preprocessed, and the feature space was reduced to include the most important features. To predict poor outcomes, machine learning models were trained and cross-validat"],"journal":["Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences"],"pagination":["15910199251375529"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12436348"],"repository":["biostudies-literature"],"pubmed_title":["Machine learning modeling for outcome prediction of hospitalized patients with aneurysmal subarachnoid hemorrhage."],"pmcid":["PMC12436348"],"pubmed_authors":["Zreik J","Rinaldo L","Jabal MS","Wahood W","Brinjikji W","Lanzino G","Kallmes DF","Essibayi MA","Ibrahim MK","Bilgin C","Kobeissi H"],"additional_accession":[]},"is_claimable":false,"name":"Machine learning modeling for outcome prediction of hospitalized patients with aneurysmal subarachnoid hemorrhage.","description":"PurposeAneurysmal rupture and subarachnoid hemorrhage (SAH) have an exceptionally high mortality and morbidity burden. The aim of this study was to develop interpretable machine learning models for predicting short-term poor outcomes defined by the National Inpatient Sample Subarachnoid Hemorrhage Outcome Measure (NIS-SOM).MethodsThe National Inpatient Sample (NIS) database was queried from 2008 to 2018 to identify patients diagnosed with SAH who had undergone endovascular coiling or clipping for intracranial aneurysm. Demographic, comorbidity, risk factor, and hospital characteristic variables were recorded. Variables were preprocessed, and the feature space was reduced to include the most important features. To predict poor outcomes, machine learning models were trained and cross-validat","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Sep","modification":"2026-06-01T06:24:37.651Z","creation":"2026-04-08T09:45:38.272Z"},"accession":"S-EPMC12436348","cross_references":{"pubmed":["40953192"],"doi":["10.1177/15910199251375529"]}}