Prediction of incident myocardial infarction using machine learning applied to harmonized electronic health record data
- PDF / 867,994 Bytes
- 10 Pages / 595.276 x 790.866 pts Page_size
- 77 Downloads / 163 Views
(2020) 20:252
RESEARCH ARTICLE
Open Access
Prediction of incident myocardial infarction using machine learning applied to harmonized electronic health record data Divneet Mandair1, Premanand Tiwari2, Steven Simon3, Kathryn L. Colborn4 and Michael A. Rosenberg1,3*
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, boosted gradient machines, and shallow and deep neural networks. A baseline model for comparison was a logistic regression using a limited set of ‘known’ risk factors for MI. Hyperparameters were identified using 10-fold cross-validation. Results: Twenty thousand Five hundred and ninety-one patients were diagnosed with MI compared with 2.25 million who did not. A deep neural network with random undersampling provided superior classification compared with other methods. However, the benefit of the deep neural network was only moderate, showing an F1 Score of 0.092 and AUC of 0.835, compared to a logistic regression model using only ‘known’ risk factors. Calibration for all models was poor despite adequate discrimination, due to overfitting from low frequency of the event of interest. Conclusions: Our study suggests that DNN may not offer substantial benefit when trained on harmonized data, compared to traditional methods using established risk factors for MI. Keywords: Myocardial infarction, Machine learning, Electronic health records
Introduction Cardiovascular disease (CVD) has long been a leading cause of death in the United States, with more than 900,000 deaths in 2016, a substantial portion of which were attributable to myocardial infarction (MI) [1]. Although there have * Correspondence: [email protected] 1 Division of Internal Medicine, University of Colorado School of Medicine, Aurora, CO, USA 3 Division of Cardiology and Cardiac Electrophysiology, University of Colorado School of Medicine, 12631 E. 17th Avenue, Mail Stop B130, Aurora, CO 80045, USA Full list of author information is available at the end of the article
been dramatic improvements in public health that have spurred a decline in CVD related deaths over the past several decades, incidence of CVD mortality has remained steady in recent years. As a result, considerable effort has been placed in improving risk prediction of CVD-related events [2, 3]. Despite this, clinical practice ha
Data Loading...
