在临床试验中将疫苗名称映射到疫苗本体学中,使用级联微调域特定语言模型
Jianfu Li1, Yiming Li1, Yuanyi Pan2
1The University of Texas Health Science Center at Houston.
Research square
|October 16, 2023
概括
这项研究引入了一种新的级联框架,以使用特定领域的语言模型标准化临床试验中的疫苗数据. 该方法显著提高了绘制疫苗概念的准确性,增强了知识开发和监测.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算生物学 计算生物学
- 公共卫生信息学 公共卫生信息学
背景情况:
- 疫苗对公共卫生至关重要,但临床试验数据缺乏标准化.
- 标准化挑战阻碍了自动化概念绘图,知识开发和疫苗监测.
- 现有的方法难以准确地绘制临床试验中的疫苗相关信息.
研究的目的:
- 从临床试验数据开发一个级联框架,用于从临床试验数据中对疫苗本体学 (VO) 进行增强的自动映射.
- 改善疫苗研究的标准化和知识提取.
主要方法:
- 使用临床试验,UMLS和VO开发了一个级联框架.
- 预先训练有素的PubMedBERT和注册的SAPBERT (UMLS-预先训练有素).
- 使用VO体和临床试验数据的微调模型,采用基于规则的加权整体方法.
主要成果:
- 级联框架 (CTPubMedBERT + SAPBERT + VO) 的表现优于现有的基线.
- 在前1名概念映射中获得了71.8%的准确性,在前10名中获得了90.0%的准确性.
- 从临床试验中证明了VO映射的显著改善.
结论:
- 拟议的框架显著提高了从临床试验中绘制疫苗本体学的情况.
- 利用特定领域的信息和整体方法可以改善数据标准化和分析.
- 这项工作有助于更好的疫苗相关知识开发和基于证据的决策.
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