Machine learning to predict mortality for aneurysmal subarachnoid hemorrhage (aSAH) using a large nationwide EHR database.

ABSTRACT: Aneurysmal subarachnoid hemorrhage (aSAH) develops quickly once it occurs and threatens the life of patients. We aimed to use machine learning to predict mortality for SAH patients at an early stage which can help doctors make clinical decisions. In our study, we applied different machine learning methods to an aSAH cohort extracted from a national EHR database, the Cerner Health Facts EHR database (2000-2018). The outcome of interest was in-hospital mortality, as either passing away while still in the hospital or being discharged to hospice care. Machine learning-based models were primarily evaluated by the area under the receiver operating characteristic curve (AUC). The population size of the SAH cohort was 6728. The machine learning methods achieved an average of AUCs of 0.805 for pred

SUBMITTER: Zhu G 

PROVIDER: S-EPMC10699620 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature