Learning from Longitudinal Data in Electronic Health Record and Genetic Data to Improve Cardiovascular Event Prediction

• Published in: Scientific reports Vol. 9; no. 1; p. 717
• Main Authors: Zhao, Juan, Feng, QiPing, Wu, Patrick, Lupu, Roxana A., Wilke, Russell A., Wells, Quinn S., Denny, Joshua C., Wei, Wei-Qi
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 24.01.2019; Nature Publishing Group
• Subjects: 639/705/1042; 692/308/409; 692/699/75; Adult; Algorithms; Cardiovascular diseases; Cardiovascular Diseases - diagnosis; Cardiovascular Diseases - epidemiology; Cardiovascular Diseases - etiology; Case-Control Studies; Cross-Sectional Studies; Deep Learning; Electronic Health Records - statistics & numerical data; Electronic medical records; Female; Genetic Variation; Humanities and Social Sciences; Humans; Learning algorithms; Long short-term memory; Longitudinal Studies; Machine Learning; Male; multidisciplinary; Neural networks; Neural Networks, Computer; Risk Factors; Science; Science (multidisciplinary); United States - epidemiology; United States
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-018-36745-x

Current approaches to predicting a cardiovascular disease (CVD) event rely on conventional risk factors and cross-sectional data. In this study, we applied machine learning and deep learning models to 10-year CVD event prediction by using longitudinal electronic health record (EHR) and genetic data. Our study cohort included 109, 490 individuals. In the first experiment, we extracted aggregated and longitudinal features from EHR. We applied logistic regression, random forests, gradient boosting trees, convolutional neural networks (CNN) and recurrent neural networks with long short-term memory (LSTM) units. In the second experiment, we applied a late-fusion approach to incorporate genetic features. We compared the performance with approaches currently utilized in routine clinical practice – American College of Cardiology and the American Heart Association (ACC/AHA) Pooled Cohort Risk Equation. Our results indicated that incorporating longitudinal feature lead to better event prediction. Combining genetic features through a late-fusion approach can further improve CVD prediction, underscoring the importance of integrating relevant genetic data whenever available.