Patient Health Representation Learning via Correlational Sparse Prior of Medical Features

Author:

Ma Xinyu1ORCID,Wang Yasha2,Chu Xu3,Ma Liantao1,Tang Wen4,Zhao Junfeng1,Yuan Ye5ORCID,Wang Guoren5ORCID

Affiliation:

1. Key Laboratory of High Confidence Software Technologies, Ministry of Education, Beijing, China

2. National Engineering Research Center of Software Engineering, Peking University, Beijing, China

3. Department of Computer Science, Tsinghua University, Beijing, China

4. Division of Nephrology, Peking University Third Hospital, Beijing, China

5. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Computer Science Applications,Information Systems

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. ProtoMix: Augmenting Health Status Representation Learning via Prototype-based Mixup;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24

2. Graph neural networks for clinical risk prediction based on electronic health records: A survey;Journal of Biomedical Informatics;2024-03

3. Attention-Based Deep Learning Model for Prediction of Major Adverse Cardiovascular Events in Peritoneal Dialysis Patients;IEEE Journal of Biomedical and Health Informatics;2024-02

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