在使用大型语言模型的过量死亡监测中改善药物识别
ArXiv
|July 25, 2025
概括
自然语言处理 (NLP) 模型,特别是BioClinicalBERT,从自由文本报告中准确地分类涉及药物过量死亡. 这增强了及时监测,克服了手动编码对新出现的物质使用趋势的局限性.
科学领域:
- 公共卫生监督 公共卫生监督
- 计算语言学 计算语言学
- 数据科学数据科学数据科学
背景情况:
- 与毒品相关的死亡,主要是芬太尼驱动的,需要在美国进行快速和精确的监测.
- 目前的方法依赖于手动编码验尸官报告进入ICD-10分类,导致数据延迟和丢失.
- 现有的自然语言处理 (NLP) 用于过量监测的应用程序已经显示出局限性.
研究的目的:
- 评估和比较各种NLP模型,以从非结构化的死亡证明文本中对特定药物参与进行分类.
- 评估传统机器学习,广域BERT和大型语言模型 (LLM) 与微调的临床NLP模型的性能.
- 确定NLP在自动化和增强过量监测数据提取方面的有效性.
主要方法:
- 为了培训和内部测试,利用了2020年美国35,433起死亡记录的数据集.
- 在一个单独的数据集上进行了外部验证,其中包括从2023年到2024年的3335个记录.
- 比较传统分类器,双向编码器表示从变压器 (BERT),BioClinicalBERT,Qwen 3和拉玛3使用宏观平均F1分数.
主要成果:
- 微调的BioClinicalBERT模型在内部测试组中实现了近乎完美的性能 (宏 F1 >=0.998).
- 外部验证证明了BioClinicalBERT的稳定性 (宏 F1=0.966),优于其他评估的模型.
- NLP模型的表现明显优于传统的机器学习和一般领域的BERT和LLMs.
结论:
- 微调的临床NLP模型,如BioClinicalBERT,为从自由文本报告中分类涉及药物过量死亡提供了高度准确和可扩展的解决方案.
- 这些NLP方法可以大幅加快监控工作流程,超越手动ICD-10编码的局限性.
- 该方法支持几乎实时检测新出现的物质使用趋势,改善公共卫生响应.
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