在杂的临床文本中用于概念识别的术语BLAST类调整工具
Tudor Groza1,2, Honghan Wu3, Marcel E Dinger4,5
1Rare Care Centre, Perth Children's Hospital, Nedlands, WA 6009, Australia.
Bioinformatics (Oxford, England)
|November 24, 2023
概括
一种新的方法,Term-BLAST-like alignment tool (TBLAT),可以改善临床文本中的概念识别. TBLAT在出版物中提高了10%的回忆力,在电子健康记录中提高了20%,超过了现有的工具.
科学领域:
- 生物医学信息学 生物医学信息学
- 自然语言处理自然语言处理.
- 计算生物学 计算生物学
背景情况:
- 概念识别 (CR) 方法通常根据标准医学文献进行评估.
- 电子健康记录 (EHR) 为CR带来了独特的挑战,原因是非标准的文本,如拼写错误和缩写.
研究的目的:
- 开发和评估一种用于临床文本中的概念识别的新方法,特别是解决EHR数据中的挑战.
- 提高生物医学和临床文本挖掘中实体链接的准确性和效率.
主要方法:
- 开发了一个类似Term-BLAST的对齐工具 (TBLAT),灵感来自生物序列对齐的BLAST算法.
- TBLAT使用k-mer计数和临床笔记中的排版错误的黄金标准库来得分潜在的概念匹配.
- 该方法在实验中与五种已建立的工具进行了比较,使用科学出版物和EHR记录.
主要成果:
- 与现有方法相比,TBLAT在科学出版物上的回忆率增加了10%.
- 在EHR记录上,TBLAT实现了显著的20%的回忆增长,突出了其在非标准临床文本上的有效性.
- 该工具可以独立使用或作为补充组件来增强当前CR方法.
结论:
- 与Term-BLAST类似的调整工具 (TBLAT) 在临床文本中的概念识别方面提供了实质性的改进,特别是在具有挑战性的EHR环境中.
- 基于序列对齐的TBLAT方法有效地处理临床笔记中常见的变异和错误,增强实体链接.
- 现象Java库提供了TBLAT的可访问的实现,用于人类现象形体学术语识别.
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