用大模型提取化疗记录，构建患者治疗时间线。
UW-BioNLP at ChemoTimelines 2025: Thinking, Fine-Tuning, and Dictionary-Enhanced LLM Systems for Chemotherapy Timeline Extraction
- 分两步走：先提取单条记录事件，再整合成患者级时间线。
- 微调Qwen3-14B模型在测试集上取得0.678的最优得分。
- 结合思维链与词典增强，提升复杂病例的事件识别能力。
ChemoTimelines共享任务旨在评估从癌症患者电子病历中构建系统性抗癌治疗时间线的方法。本文介绍我们在子任务2——从原始临床笔记生成患者化疗时间线上的方法、结果与发现。我们评估了思维链推理、监督微调、直接偏好优化及词典增强查找等多种策略以提升时间线抽取性能。所有方法均采用两步流程：首先由大模型从单个临床笔记中提取化疗事件，随后通过算法对事件进行归一化与聚合，形成患者级时间线。各方法在大模型的使用与训练方式上有所差异。多种方案在测试集排行榜上表现优异，其中微调后的Qwen3-14B模型取得0.678的最高官方分数。研究结果与分析为该任务及类似任务的设计提供了有益参考。
原文摘要 · Abstract (English)
The ChemoTimelines shared task benchmarks methods for constructing timelines of systemic anticancer treatment from electronic health records of cancer patients. This paper describes our methods, results, and findings for subtask 2 -- generating patient chemotherapy timelines from raw clinical notes. We evaluated strategies involving chain-of-thought thinking, supervised fine-tuning, direct preference optimization, and dictionary-based lookup to improve timeline extraction. All of our approaches followed a two-step workflow, wherein an LLM first extracted chemotherapy events from individual clinical notes, and then an algorithm normalized and aggregated events into patient-level timelines. Each specific method differed in how the associated LLM was utilized and trained. Multiple approaches yielded competitive performances on the test set leaderboard, with fine-tuned Qwen3-14B achieving the best official score of 0.678. Our results and analyses could provide useful insights for future attempts on this task as well as the design of similar tasks.
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