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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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer Nature
2025-02-01
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| Series: | Human-Centric Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44230-025-00093-7 |
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