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

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....

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Bibliographic Details
Published in:Scientific reports 2019-01, Vol.9 (1), p.717, Article 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: Article
Language:English
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
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Summary: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.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-018-36745-x