Prediction of incident myocardial infarction using machine learning applied to harmonized electronic health record data.

ABSTRACT:

Background

With cardiovascular disease increasing, substantial research has focused on the development of prediction tools. We compare deep learning and machine learning models to a baseline logistic regression using only 'known' risk factors in predicting incident myocardial infarction (MI) from harmonized EHR data.

Methods

Large-scale case-control study with outcome of 6-month incident MI, conducted using the top 800, from an initial 52 k procedures, diagnoses, and medications within the UCHealth system, harmonized to the Observational Medical Outcomes Partnership common data model, performed on 2.27 million patients. We compared several over- and under- sampling techniques to address the imbalance in the dataset. We compared regularized logistics regression, random forest

SUBMITTER: Mandair D 

PROVIDER: S-EPMC7532582 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature