ResearchResearch paperLarge Language Models · Reinforcement Learning1 source · Oct 18, 2026

ClinSDT: LLM-Encoded Clinical Semantic Guidance for Sepsis Treatment via Decision Transformer

To address this limitation, we propose ClinSDT, an LLM-assisted Decision Transformer framework for offline sepsis treatment.

Offline sepsis treatment aims to learn treatment policies from retrospective ICU trajectories.
Decision Transformer-based methods provide a natural sequence modeling framework for this task, but they mainly rely on raw physiological states and historical clinician actions.
During inference, ClinSDT performs closed-loop treatment rollout by refreshing semantic inputs from the currently available patient state.
Experiments on MIMIC-III and MIMIC-IV show that ClinSDT achieves consistent relative improvements over representative imitation learning, offline reinforcement learning, and Decision Transformer baselines under the same rollout protocol.

[1]ClinSDT: LLM-Encoded Clinical Semantic Guidance for Sepsis Treatment via Decision Transformer
arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 18, 08:57 AM
To address this limitation, we propose ClinSDT, an LLM-assisted Decision Transformer framework for offline sepsis treatment.
Offline sepsis treatment aims to learn treatment policies from retrospective ICU trajectories.

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