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

Gen Zhu, Anthony Yuan, Duo Yu, Alicia Zha and Hulin Wu

PLOS Digital Health, 2023, vol. 2, issue 12, 1-10

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 predicting mortality with only the initial 24 hours’ EHR data. Without losing the prediction power, we used the logistic regression to identify 42 risk factors, —examples include age and serum glucose—that exhibit a significant correlation with the mortality of aSAH patients. Our study illustrates the potential of utilizing machine learning techniques as a practical prognostic tool for predicting aSAH mortality at the bedside.Author summary: The enormous amount of data available in the EHR offers an excellent environment for applying and developing machine learning and artificial intelligence (AI) methods to solve clinical research problems. In our study, we aim to use machine learning and full clinical information to predict in-hospital mortality for an aSAH cohort obtained from a national EHR database. For aSAH which is a severe type of stroke, most studies used variables such as age, size and location of the ruptured aneurysm to predict the death. They did not consider machine learning methods as the prediction tools and fully utilize the available clinical variables which can be over 1,000. We will show how modern machine learning methods can be employed to conduct the bedside prediction for mortality after aSAH.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pdig00:0000400

DOI: 10.1371/journal.pdig.0000400

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