通过使用临床自然语言处理模型,改善过量死亡监测中的药物识别
Arthur J Funnell1, Panayiotis Petousis1, Fabrice Harel-Canada2
1Medical & Imaging Informatics Group, Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA.
Journal of forensic sciences
|February 9, 2026
概括
精确的药物相关死亡监测至关重要. 自然语言处理 (NLP) 模型,特别是BioClinicalBERT,可以从验尸官报告中快速分类过量数据,改善公共卫生监测.
科学领域:
- 公共卫生 公共卫生
- 计算语言学 计算语言学
- 法医科学 法医科学 法医科学
背景情况:
- 与毒品相关的死亡,特别是芬太尼引起的死亡,在美国正在上升,需要有效的监测.
- 目前的方法依赖于将验尸官报告手动编码到ICD-10,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,从非结构化文本对药物参与过量死亡的分类.
- 评估传统机器学习,基于BERT的模型和大型语言模型 (LLM) 的性能.
- 确定最准确和可扩展的NLP方法,以加强过量监测.
主要方法:
- 为了培训和内部测试,利用了2020年美国35,433份死亡记录的数据集.
- 在一个单独的数据集上进行了外部验证,该数据集包含了2023-2024年的3335个记录.
- 比较传统的分类器,微调的变压器 (BERT) 和生物临床BERT的双向编码器表示,以及仅用解码器的LLM (Qwen 3,Llama 3).
- 通过宏观平均F1分数和95%的置信区间来评估性能.
主要成果:
- 微调的BioClinicalBERT模型在内部数据上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),优于其他模型.
- NLP模型显著超过了传统的机器学习和广域BERT.
结论:
- 微调的临床NLP模型,如BioClinicalBERT,提供了一个高度准确和可扩展的解决方案,用于从自由文本报告中对过量死亡进行分类.
- 这些NLP方法可以大幅加快监控,克服手动编码的局限性.
- 该研究支持通过先进的NLP技术近乎实时检测新兴物质使用趋势.
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