Machine learning and deep learning to predict mortality in patients with spontaneous coronary artery dissection.

ABSTRACT: Machine learning (ML) and deep learning (DL) can successfully predict high prevalence events in very large databases (big data), but the value of this methodology for risk prediction in smaller cohorts with uncommon diseases and infrequent events is uncertain. The clinical course of spontaneous coronary artery dissection (SCAD) is variable, and no reliable methods are available to predict mortality. Based on the hypothesis that machine learning (ML) and deep learning (DL) techniques could enhance the identification of patients at risk, we applied a deep neural network to information available in electronic health records (EHR) to predict in-hospital mortality in patients with SCAD. We extracted patient data from the EHR of an extensive urban health system and applied several ML and DL mode

SUBMITTER: Krittanawong C 

PROVIDER: S-EPMC8076284 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature