Abstract:
: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.
journal_name
Sci Repjournal_title
Scientific reportsauthors
Zhao J,Feng Q,Wu P,Lupu RA,Wilke RA,Wells QS,Denny JC,Wei WQdoi
10.1038/s41598-018-36745-xsubject
Has Abstractpub_date
2019-01-24 00:00:00pages
717issue
1issn
2045-2322pii
10.1038/s41598-018-36745-xjournal_volume
9pub_type
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