从临床PDF文档中无监督提取身体文本
Adel Bensahla1,2, Jamil Zaghir1,2, Christophe Gaudet-Blavignac1,2
1Division of Medical Information Sciences, Geneva University Hospitals, Switzerland.
Studies in health technology and informatics
|August 23, 2024
概括
本研究介绍了一种无监督的算法,用于自动从临床PDF中提取体文本. 这种新的方法实现了高精度和回忆,改善了自然语言处理 (NLP) 任务的数据可访问性.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理 (NLP) 是一种自然语言处理.
- 数据科学数据科学数据科学
背景情况:
- 临床PDF文档包含重要信息,但为NLP任务提取体文本是具有挑战性的.
- 现有的文本提取方法往往需要人工干预或监督学习.
研究的目的:
- 开发一个无监督的算法,从临床PDF文档中自动提取体文本.
- 提高下游自然语言处理 (NLP) 应用程序临床数据的可访问性.
主要方法:
- 使用DBSCAN集群,应用于汇总的页面数据.
- 利用文本内容和协调信息来组织文本块.
- 采用无监督学习方法来处理大量数据.
主要成果:
- 实现了高精度得分,范围从0.82到0.98.
- 在0.62和0.94.9之间显示出强的召回分数.
- 报告的F1分数从0.71到0.96跨越不同的医学专业.
结论:
- 无监督的算法有效地从临床PDF中提取体文本.
- 该方法显示了在医疗领域改善NLP任务的巨大潜力.
- 未来的工作将专注于动态参数调整和扩展与更大的数据集.
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