命名实体识别和规范化阿尔茨海默病的资格标准
1School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas.
Proceedings. IEEE International Conference on Healthcare Informatics
|January 29, 2024
概括
本研究引入了使用自然语言处理 (NLP) 的新管道,以有效地从阿尔茨海默病 (AD) 临床试验资格标准中提取信息,改善患者招募和数据管理.
科学领域:
- 计算语言学计算语言学
- 生物医学信息学是生物医学信息学.
- 临床试验方法论 临床试验方法论
背景情况:
- 阿尔茨海默氏症 (AD) 影响全球数百万人,需要对新疗法进行高效的临床试验.
- 鉴定适用于AD试验的合格参与者是一个重要的瓶,因为在处理资格标准 (EC) 方面存在挑战.
- 生物医学文本现有的自然语言处理 (NLP) 模型往往缺乏临床试验所需的特定实体类型和否定处理.
研究的目的:
- 开发和评估一条自动化管道,用于从AD临床试验EC中提取和规范信息.
- 提高患者招募效率,并实现临床试验数据的数据公平性 (可查找,可访问,可互操作,可重复使用).
- 解决当前NLP模型关于专业实体类型和EC中否定术语的规范化方面的局限性.
主要方法:
- 开发了一个信息提取管道,包括EC数据的预处理,临床命名实体识别 (NER) 和生物医学命名实体规范化 (NEN) 到统一医疗语言系统 (UMLS).
- 该NER模型旨在确定七个预定义的实体类别,这些实体类别与临床试验EC相关.
- 该NEN模型包含了精确和部分匹配策略,以及定制的规则,以准确规范实体,包括负前的实体.
主要成果:
- 该NER组件获得了0.816的整体F1评分,在临床试验EC.中识别七种关键实体类型时表现出高准确度.
- 在标准化实体中,NEN组件实现了0.940的准确性,通过专门的规则和匹配策略有效处理被否定的术语.
- 该管道成功地将临床试验EC中的命名实体与最佳准确度正常化,验证了其性能.
结论:
- 开发的NLP管道显著提高了从阿尔茨海默病临床试验资格标准中提取信息的效率和准确性.
- 管道处理特定实体类型和否定术语的能力解决了现有的生物医学NLP模型的关键局限性.
- 这种方法促进了半自动的患者招募,并有助于改善临床试验研究中的数据公平性.
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