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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| Published in | Scientific reports Vol. 9; no. 1; p. 717 |
|---|---|
| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
24.01.2019
Nature Publishing Group |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2045-2322 2045-2322 |
| DOI | 10.1038/s41598-018-36745-x |
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| 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. |
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| AbstractList | 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. 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.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. |
| ArticleNumber | 717 |
| Author | Feng, QiPing Denny, Joshua C. Wu, Patrick Wilke, Russell A. Wells, Quinn S. Zhao, Juan Lupu, Roxana A. Wei, Wei-Qi |
| Author_xml | – sequence: 1 givenname: Juan surname: Zhao fullname: Zhao, Juan organization: Department of Biomedical Informatics, Vanderbilt University Medical Center – sequence: 2 givenname: QiPing surname: Feng fullname: Feng, QiPing organization: Division of Clinical Pharmacology, Vanderbilt University Medical Center – sequence: 3 givenname: Patrick surname: Wu fullname: Wu, Patrick organization: Department of Biomedical Informatics, Vanderbilt University Medical Center, Medical Scientist Training Program, Vanderbilt University School of Medicine – sequence: 4 givenname: Roxana A. orcidid: 0000-0002-6316-1835 surname: Lupu fullname: Lupu, Roxana A. organization: Department of Medicine, University of South Dakota Sanford School of Medicine – sequence: 5 givenname: Russell A. surname: Wilke fullname: Wilke, Russell A. organization: Department of Medicine, University of South Dakota Sanford School of Medicine – sequence: 6 givenname: Quinn S. surname: Wells fullname: Wells, Quinn S. organization: Department of Medicine, Vanderbilt University Medical Center – sequence: 7 givenname: Joshua C. surname: Denny fullname: Denny, Joshua C. organization: Department of Biomedical Informatics, Vanderbilt University Medical Center, Department of Medicine, Vanderbilt University Medical Center – sequence: 8 givenname: Wei-Qi surname: Wei fullname: Wei, Wei-Qi email: wei-qi.wei@vumc.org organization: Department of Biomedical Informatics, Vanderbilt University Medical Center |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30679510$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | 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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| Title | Learning from Longitudinal Data in Electronic Health Record and Genetic Data to Improve Cardiovascular Event Prediction |
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