High-Risk Prediction of Cardiovascular Diseases via Attention-Based Deep Neural Networks

High-risk prediction of cardiovascular disease is of great significance and impendency in medical fields with the increasing phenomenon of sub-health these years. Most existing pathological methods for the prognosis prediction are either costly or prone to misjudgement. Therefore, plenty of automate...

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Bibliographic Details
Published in:IEEE/ACM transactions on computational biology and bioinformatics 2021-05, Vol.18 (3), p.1093-1105
Main Authors: An, Ying, Huang, Nengjun, Chen, Xianlai, Wu, FangXiang, Wang, Jianxin
Format: Article
Language:English
Subjects:
Adult
Algorithms
Artificial neural networks
attention mechanism
Cardiovascular disease
Cardiovascular diseases
Cardiovascular Diseases - diagnosis
Data Mining
Data models
Deep Learning
deep neural network
Electronic Health Records
Electronic medical records
Feature extraction
Female
Health risks
High-risk prediction
Humans
Learning algorithms
Machine learning
Male
Medical diagnostic imaging
Medical Informatics - methods
Middle Aged
Neural networks
Neural Networks, Computer
Predictions
Predictive models
Risk
Risk Assessment
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Summary:High-risk prediction of cardiovascular disease is of great significance and impendency in medical fields with the increasing phenomenon of sub-health these years. Most existing pathological methods for the prognosis prediction are either costly or prone to misjudgement. Therefore, plenty of automated models based on machine learning have been proposed to predict the onset of cardiovascular disease with the premorbid information of patients extracted from their historical Electronic Health Records (EHRs). However, it is a tough job to select proper features from longitudinal and heterogeneous EHRs, and also a great challenge to obtain accurate and robust representations for patients. In this paper, we propose an entirely end-to-end model called DeepRisk based on attention mechanism and deep neural networks, which can not only learn high-quality features automatically from EHRs, but also efficiently integrate heterogeneous and time-ordered medical data, and finally predict patients' risk of cardiovascular diseases. Experiments are carried out on a real medical dataset and results show that DeepRisk can significantly improve the high-risk prediction accuracy for cardiovascular disease compared with state-of-the-art approaches.
ISSN:1545-5963
1557-9964
DOI:10.1109/TCBB.2019.2935059