TECM*: A Data-Driven Assessment to Reinforcement Learning Methods and Application to Heparin Treatment Strategy for Surgical Sepsis

Jiang Liu; Yujie Li; Chan Zhou; Yihao Xie; Qilong Sun; Xin Shu; Peiwei Li; Chunyong Yang; Yiziting Zhu; Jiaqi Zhu; Yuwen Chen; Bo An; Hao Wu; Bin Yi

arXiv · 2512.10973

TECM*: A Data-Driven Assessment to Reinforcement Learning Methods and Application to Heparin Treatment Strategy for Surgical Sepsis

Jiang Liu·Yujie Li·Chan Zhou·Yihao Xie·Qilong Sun·Xin Shu·Peiwei Li·Chunyong Yang·Yiziting Zhu·Jiaqi Zhu·Yuwen Chen·Bo An·Hao Wu·Bin Yi

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Abstract

Objective: Sepsis is a life-threatening condition caused by severe infection leading to acute organ dysfunction. This study proposes a data-driven metric and a continuous reward function to optimize personalized heparin therapy in surgical sepsis patients. Methods: Data from the MIMIC-IV v1.0 and eICU v2.0 databases were used for model development and evaluation. The training cohort consisted of abdominal surgery patients receiving unfractionated heparin (UFH) after postoperative sepsis onset. We introduce a new RL-based framework: converting the discrete SOFA score to a continuous cxSOFA for more nuanced state and reward functions; Second, defining "good" or "bad" strategies based on cxSOFA by a stepwise manner; Third, proposing a Treatment Effect Comparison Matrix (TECM), analogous to a confusion matrix for classification tasks, to evaluate the treatment strategies. We applied different RL algorithms, Q-Learning, DQN, DDQN, BCQ and CQL to optimize the treatment and comprehensively evaluated the framework. Results: Among the AI-derived strategies, the cxSOFA-CQL model achieved the best performance, reducing mortality from 1.83% to 0.74% with the average hospital stay from 11.11 to 9.42 days. TECM demonstrated consistent outcomes across models, highlighting robustness. Conclusion: The proposed RL framework enables interpretable and robust optimization of heparin therapy in surgical sepsis. Continuous cxSOFA scoring and TECM-based evaluation provide nuanced treatment assessment, showing promise for improving clinical outcomes and decision-support reliability.

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Jiang Liu, Yujie Li, Chan Zhou, Yihao Xie, Qilong Sun, Xin Shu, Peiwei Li, Chunyong Yang, Yiziting Zhu, Jiaqi Zhu, Yuwen Chen, Bo An, Hao Wu, Bin Yi. 2025-12-02. TECM*: A Data-Driven Assessment to Reinforcement Learning Methods and Application to Heparin Treatment Strategy for Surgical Sepsis. https://arxiv.org/abs/2512.10973

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