UW-BioNLP在ChemoTimelines 2025中的表现：基于思考、微调和词典增强的LLM系统用于化疗时间线提取
UW-BioNLP at ChemoTimelines 2025: Thinking, Fine-Tuning, and Dictionary-Enhanced LLM Systems for Chemotherapy Timeline Extraction
AI总结：
UW-BioNLP通过思考、微调和词典增强LLM方法，在ChemoTimelines 2025中实现了最佳性能，提升了化疗时间线提取的准确性。
摘要：
ChemoTimelines共享任务基准方法用于从癌症患者的电子健康记录中构建系统抗癌治疗的时间线。本文描述了我们针对子任务2--从原始临床笔记中生成患者化疗时间线的方法、结果和发现。我们评估了涉及链式思考、监督微调、直接偏好优化和基于词典的查找策略，以提高时间线提取的效果。所有我们的方法都遵循两步工作流程，其中LLM首先从个体临床笔记中提取化疗事件，然后算法对事件进行标准化和聚合，生成患者层面的时间线。每种具体方法在如何利用和训练关联的LLM方面有所不同。多种方法在测试集排行榜上取得了有竞争力的表现，微调后的Qwen3-14B在官方得分上取得了最佳成绩0.678。我们的结果和分析可以为未来在此任务上的尝试以及类似任务的设计提供有用的见解。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
