EAPR: explainable and augmented patient representation learning for disease prediction-Reference-Cited by-同舟云学术

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EAPR: explainable and augmented patient representation learning for disease prediction

Published:2023-11-14 Issue:1 Volume:11 Page:
ISSN:2047-2501
Container-title:Health Information Science and Systems
language:en
Short-container-title:Health Inf Sci Syst

Author:

Zhang Jiancheng,Xu Yonghui,Ye Bicui,Zhao Yibowen,Sun Xiaofang,Meng Qi,Zhang Yang,Cui Lizhen

Funder

National Key R &D Program of China

NSFC

Shandong Provincial Natural Science Foundation

Shandong Provincial Outstanding Youth Science Foundation

Fundamental Research Funds of Shandong University

Publisher

Springer Science and Business Media LLC

Subject

General Medicine

Link

https://link.springer.com/content/pdf/10.1007/s13755-023-00256-5.pdf

Reference37 articles.

1. Wang T, Bendayan R, Msosa Y, Pritchard M, Roberts A, Stewart R, Dobson R. Patient-centric characterization of multimorbidity trajectories in patients with severe mental illnesses. J Biomed Inform. 2022;127:104010.

2. Ma F, Yu L, Ye L, Yao DD, Zhuang W. Length-of-stay prediction for pediatric patients with respiratory diseases using decision tree methods. IEEE J Biomed Health Inform. 2020;24(9):2651–62. https://doi.org/10.1109/JBHI.2020.2973285.

3. Zheng Z, Wang C, Xu T, Shen D, Chen E. Drug package recommendation via interaction-aware graph induction. 2021.

4. Lu H, Uddin S, Hajati F, Moni MA, Khushi M. A patient network-based machine learning model for disease prediction: the case of type 2 diabetes mellitus. Appl Intell. 2022;52(3):2411–22.

5. Fan Y, Tao Z, Lin J, Chen H. An encoder-decoder network for automatic clinical target volume target segmentation of cervical cancer in CT images. Int J Crowd Sci. 2022;6(3):111–6.

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