ODD:基于自然语言处理的基准数据集,用于检测与阿片类药物相关的异常行为
Sunjae Kwon1, Xun Wang2, Weisong Liu3
1UMass Amherst.
概括
本研究介绍了ODD数据集,用于从电子健康记录中检测与阿片类药物相关的异常行为 (ORAB). 快速调整的NLP模型显示出前景,性能优于微调,特别是在识别不太常见的ORAB方面.
科学领域:
- 生物医学自然语言处理
- 临床信息学 临床信息学
- 阿片类药物使用障碍研究研究
背景情况:
- 与阿片类药物相关的异常行为 (ORAB) 是阿片类药物过量发生的重要风险因素.
- 从临床笔记中准确识别ORAB对于患者安全至关重要.
- 现有的ORAB检测方法缺乏全面的数据集.
研究的目的:
- 介绍与阿片类药物相关的异常行为检测 (ODD) 数据集,这是一个新的生物医学NLP资源.
- 促进用于ORAB识别的NLP模型的开发和评估.
- 在电子健康记录中将ORAB分为九个不同的类别.
主要方法:
- 开发了一个由专家注释的数据集 (ODD),包括患者的电子病历笔记.
- 使用了两种最先进的NLP方法:微调和提示调.
- 在九个ORAB类别中评估模型性能,使用精度回忆曲线下的宏观平均面积.
主要成果:
- 提示调整NLP模型在ORAB检测中通常表现优于微调模型.
- 在识别不太常见的ORAB类别时,性能增长最为明显.
- 最好的模型在精度回忆曲线下达到88.17%的宏观平均面积.
结论:
- ODD数据集为推进ORAB检测研究提供了宝贵的资源.
- 快速调整在识别各种ORAB,包括罕见的实例中表现出卓越的性能.
- 为了更好地检测不常见的ORAB类别,需要进一步改进.
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