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

Ma Liantao; Zhang Chaohe; Jiao Xianfeng; Wang Yasha; Tang Wen; Zhao Junfeng

doi:10.7544/issn1000-1239.2021.20211022

Journal of Computer Research and Development > 2021 > 58(12): 2645-2659. > DOI: 10.7544/issn1000-1239.2021.20211022 CSTR: 32373.14.issn1000-1239.2021.20211022

Ma Liantao, Zhang Chaohe, Jiao Xianfeng, Wang Yasha, Tang Wen, Zhao Junfeng. Dr.Deep: Interpretable Evaluation of Patient Health Status via Clinical Feature’s Context Learning[J]. Journal of Computer Research and Development, 2021, 58(12): 2645-2659. DOI: 10.7544/issn1000-1239.2021.20211022

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

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)

Funds: 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).

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Published Date: November 30, 2021

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Abstract

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
- electronic medical record,
- clinical prognosis,
- healthcare analysis,
- deep learning,
- interpretability

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