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从放射学和病理学笔记中提取概念测量的NLP框架

Annie Bowles^1,2, Cris Perez^1,2, Anil Vachani³

¹VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT.

Studies in health technology and informatics

|January 25, 2024

概括

我们创建了一个癌症不可知自然语言处理 (NLP) 工具,以识别癌症临床途径的患者. 这个自动化系统有效地从乳腺癌,肺癌和前列腺癌患者的报告中提取关键数据.

科学领域:

计算瘤学是一种计算瘤学.
临床信息学是一种临床信息学.
自然语言处理 (NLP) 是一种自然语言处理.

背景情况:

确定适合癌症临床途径的患者对于最佳治疗至关重要.
手动审查病理学和放射学报告耗时且容易出现错误.
需要自动化方法来简化临床试验和向治疗的患者识别.

研究的目的:

开发和验证一种基于规则的NLP框架,无关癌症.
从病理学和放射学报告中提取关键维度和测量.
有效地部署用于识别符合条件的患者乳腺癌,肺癌和前列腺癌临床途径的框架.

主要方法:

开发了一个基于规则的NLP框架,旨在对癌症无知.
验证了框架从非结构化文本中提取特定概念和测量的能力.
部署了NLP框架用于自动化患者队列识别.

主要成果:

该NLP框架成功地从病理学和放射学报告中提取了所需的数据尺寸.
该系统在识别符合条件的患者方面表现出了效率和成本效益.
成功部署用于识别患者的乳腺癌,肺癌和前列腺癌途径.

结论:

癌症不可知性NLP框架可以自动识别特定癌症临床途径的患者.
开发的工具为临床途径资格查提供了一种高效和具有成本效益的解决方案.
这种方法有可能改善患者获得相关癌症护理途径的机会.

关键词:

自然语言处理自然语言处理.临床途径临床途径一个基本的框架框架.肺部结节肺部结节病理学笔记病理学笔记病理学笔记前列腺的体积,前列腺的体积.瘤的大小瘤大小

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