在命名实体识别领域的领域专业知识和机器智能的推断差距:从与物质使用相关的数据集中创建和洞察物质使用相关的数据集
Sumon Kanti Dey1, Jeanne M Powell1, Azra Ismail1
1Department of Biomedical Informatics, Emory University, Atlanta, GA 30322, USA.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|February 27, 2026
概括
这项研究开发了一个命名实体识别 (NER) 框架,以识别来自社交媒体的非医疗类阿片类药物使用的临床和社会影响. 一个微调的DeBERTa-large模型的表现优于大型语言模型,显示了成监控的潜力.
科学领域:
- 自然语言处理自然语言处理.
- 公共卫生 公共卫生
- 计算语言学 计算语言学
背景情况:
- 非医疗性阿片类药物使用带来了重大的公共卫生挑战,其临床和社会后果报告不足.
- 社交媒体提供了与阿片类药物使用影响相关的第一人称体验的丰富来源.
- 现有的方法来从社交媒体中提取这些影响是有限的.
研究的目的:
- 开发和评估一个命名实体识别 (NER) 框架,以从社交媒体中提取非医疗性阿片类药物使用的自我报告的临床和社会影响.
- 为了介绍RedditImpacts 2.0,这项任务的精细数据集.
- 为了比较基于微调编码器模型和大型语言模型 (LLM) 的性能.
主要方法:
- 开发了一个NER框架,以从社交媒体叙述中识别临床影响和社会影响.
- 创建并使用了RedditImpacts 2.0数据集,并改进了注释准则.
- 评估精细调整的DeBERTa-large模型和LLM在零和少数镜头设置中.
主要成果:
- 微调的DeBERTa-large模型实现了0.61的放松的代币级F1,在精度和跨度准确性方面表现优于LLM.
- 通过减少标记数据实现了有效的NER性能,这表明对资源有限的设置的可行性.
- 最好的模型和专家协议之间仍然存在业绩差距 (科恩的kappa:0.81).
结论:
- 对特定领域的微调对于临床NLP任务是有价值的,它增强了用于成监测和医疗保健决策的AI工具.
- 开发的框架和数据集有助于在公共卫生领域负责任的人工智能发展.
- 需要进一步的研究来弥合人工智能能力和专家层面的理解之间的差距.
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