自然语言处理工具用于从暴力死亡报告系统中的案例叙述中提取有关美国阿片类药物过量使用的信息
Elise Omaki1, Felipe Restrepo2, Wendy C Shields3
1Johns Hopkins Center for Injury Research and Policy, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA eperry@jhu.edu.
概括
一个新的工具使用自然语言处理 (NLP) 来从药物过量死亡记录中提取重要信息. 这个Excel提取器原型准确地识别了过量服用的情况,改善了监测和帮助流行病学家.
科学领域:
- 公共卫生信息学 公共卫生信息学
- 计算语言学 计算语言学
- 流行病学 流行病学
背景情况:
- 有效的药物过量监测对于识别新出现的威胁和趋势至关重要.
- 开发先进的工具是必要的,以分析过量使用致死事故的数据.
- 自然语言处理 (NLP) 为从非结构化文本中提取信息提供了一种有希望的方法.
研究的目的:
- 开发和评估一种原型NLP工具,用于从药物过量死亡记录中提取信息.
- 与手动数据抽象和机器学习模型相比,评估工具的准确性和性能.
主要方法:
- 利用来自暴力死亡报告系统的毒品过量死亡的数据.
- 从死亡记录叙述中手动标记了12个类别中的82个属性.
- 开发了一个"Excel Extractor"工具,用于从叙事中识别,提取和编码目标短语.
- 将工具的输出与手动标记的数据和机器学习模型进行比较,以确定准确性.
主要成果:
- 在12个属性中,Excel Extractor在9个属性中获得了超过0.8的F1评分.
- 该工具在12个属性中的7个方面表现优于多个机器学习模型.
- 在56%的标签上取得了0.8或更高的F1评分 (46/82),在近三分之一的标签上达到0.9或更高的F1评分 (25/82).
结论:
- 一个基于电子表格公式的NLP工具可以准确地从药物过量死亡叙述中提取信息.
- 在大多数属性上,基于规则的提取性能与深度学习相当或更好.
- 埃克塞尔提取器有可能为研究药物过量死亡的流行病学家简化数据抽象.
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