ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2021, Vol. 58 ›› Issue (12): 2645-2659.doi: 10.7544/issn1000-1239.2021.20211022

Special Issue: 2021可解释智能学习方法及其应用专题

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Dr.Deep: Interpretable Evaluation of Patient Health Status via Clinical Feature’s Context Learning

Ma Liantao1,2,3, Zhang Chaohe1,2, Jiao Xianfeng1,2, Wang Yasha1,3, Tang Wen4, Zhao Junfeng1,2   

  1. 1(Key Laboratory of High Confidence Software Technologies (Peking University), Ministry of Education, Beijing 100871);2(School of Electronics Engineering and Computer Science, Peking University, Beijing 100871);3(National Research Center of Software Engineering, Peking University, Beijing 100871);4(Division of Nephrology, Peking University Third Hospital, Beijing 100191)
  • Online:2021-12-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (62172011), PKU-Baidu Fund (2020BD030), China International Medical Foundation (Z-2017-24-2037), and China Postdoctoral Science Foundation (2021TQ0011).

Abstract: Deep-learning-based health status representation learning is a fundamental research problem in clinical prediction and has raised much research interest. Existing models have shown superior performance, but they fail to explore personal characteristics and provide fine-grained interpretability thoroughly. In this work, we develop a general health status

Key words: electronic medical record, clinical prognosis, healthcare analysis, deep learning, interpretability

CLC Number: