Offline Safe Reinforcement Learning for Sepsis Treatment: Tackling Variable-Length Episodes with Sparse Rewards

Offline Safe Reinforcement Learning for Sepsis Treatment: Tackling Variable-Length Episodes with Sparse Rewards

Abstract In critical medicine, data-driven methods that assist in physician decisions often require accurate responses and controllable safety risks. Most recent reinforcement learning models developed for clinical research typically use fixed-length and very short time series data. Unfortunately, s...

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Bibliographic Details
Main Authors: Rui Tu, Zhipeng Luo, Chuanliang Pan, Zhong Wang, Jie Su, Yu Zhang, Yifan Wang
Format: Article
Language:English
Published: Springer Nature 2025-02-01
Series:Human-Centric Intelligent Systems
Subjects:
Offline reinforcement learning
Intermediate rewards
Variable-length time series
Sepsis treatment
Online Access:https://doi.org/10.1007/s44230-025-00093-7
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https://doi.org/10.1007/s44230-025-00093-7

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