分类：用于解释性风险预测的辩证推理方法——适用于具有不规律采样数据的医学时间序列数据
TRIAGE: Dialectical Reasoning for Explainable Risk Prediction on Irregularly Sampled Medical Time Series with LLMs
摘要
基于电子健康记录的临床预警系统能够将对患者的临床观察结果记录为不规则采样的医学时间序列数据。这样的系统必须能够为患者进行分类处理时提供准确的风险评估，同时还需要提供易于理解的推理依据，以便临床医生进行验证。虽然大型语言模型已被用于这一任务，但这些模型往往会将复杂临床风险简化为过于简单的二元预测结果。这种风险的两极分化会破坏风险评估的准确性和不同患者之间的比较性。为了解决这一问题，我们提出了TRIAGE框架。该框架通过训练语言模型来生成关于各种临床结果的辩证性推理，从而得出更具说服力的评估结果。这种辩证性的推理方式有助于减轻风险的两极分化问题，使得单一语言模型能够生成基于明确临床推理的连续风险评分。在三个医学时间序列数据集上的评估结果显示，TRIAGE的平均AUPRC提升了3.3%，且校准误差减少了81%。此外，使用语言模型作为评估工具的结果表明，我们的推理结果在临床推理质量方面优于现有的基准方法，其优势高达20%。代码可以在https://github.com/HyeongWon-Jang/TRIAGE下载获取。
English Abstract
Clinical early warning systems built on electronic health records, in which clinical observations are recorded as irregularly sampled medical time series (ISMTS), must deliver both calibrated risk scores for patient triage and interpretable rationales that clinicians can verify. Large Language Models (LLMs) have been explored for this task, yet they collapse graded clinical risk into overconfident binary predictions. This risk polarization undermines both calibration and cross-patient comparability. To address this, we propose TRIAGE, a framework that trains an LLM to generate dialectical reasoning over competing clinical outcomes by eliciting outcome-specific rationales. This dialectical formulation mitigates risk polarization, enabling a single LLM to yield continuous risk scores grounded in explicit clinical reasoning. Evaluated on three ISMTS benchmarks, TRIAGE achieves an average AUPRC improvement of 3.3% and reduces calibration error by 81% compared to the competitive baselines. An LLM-as-a-judge assessment further shows that our rationales surpass post-hoc explanations from the baseline by 20% in clinical reasoning quality. The source code is available at https://github.com/HyeongWon-Jang/TRIAGE .