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CLC number: TP391.4

On-line Access: 2021-07-20

Received: 2020-03-27

Revision Accepted: 2020-06-07

Crosschecked: 2021-07-05

Cited: 0

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Citations:  Bibtex RefMan EndNote GB/T7714


Sihan Zhu


Jian Pu


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Frontiers of Information Technology & Electronic Engineering  2021 Vol.22 No.7 P.926-939


A self-supervised method for treatment recommendation in sepsis

Author(s):  Sihan Zhu, Jian Pu

Affiliation(s):  School of Computer Science and Technology, East China Normal University, Shanghai 200062, China; more

Corresponding email(s):   jianpu@fudan.edu.cn

Key Words:  Treatment recommendation, Sepsis, Self-supervised learning, Reinforcement learning, Electronic health records

Sihan Zhu, Jian Pu. A self-supervised method for treatment recommendation in sepsis[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(7): 926-939.

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T1 - A self-supervised method for treatment recommendation in sepsis
A1 - Sihan Zhu
A1 - Jian Pu
J0 - Frontiers of Information Technology & Electronic Engineering
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2000127

sepsis treatment is a highly challenging effort to reduce mortality in hospital intensive care units since the treatment response may vary for each patient. Tailored treatment recommendations are desired to assist doctors in making decisions efficiently and accurately. In this work, we apply a self-supervised method based on reinforcement learning (RL) for treatment recommendation on individuals. An uncertainty evaluation method is proposed to separate patient samples into two domains according to their responses to treatments and the state value of the chosen policy. Examples of two domains are then reconstructed with an auxiliary transfer learning task. A distillation method of privilege learning is tied to a variational auto-encoder framework for the transfer learning task between the low- and high-quality domains. Combined with the self-supervised way for better state and action representations, we propose a deep RL method called high-risk uncertainty (HRU) control to provide flexibility on the trade-off between the effectiveness and accuracy of ambiguous samples and to reduce the expected mortality. Experiments on the large-scale publicly available real-world dataset MIMIC-III demonstrate that our model reduces the estimated mortality rate by up to 2.3% in total, and that the estimated mortality rate in the majority of cases is reduced to 9.5%.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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