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在临床试验中将疫苗名称映射到疫苗本体学中,使用级联微调的域特定语言模型

Jianfu Li¹, Yiming Li², Yuanyi Pan³

¹Department of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, FL, 32224, USA.

Journal of biomedical semantics

|August 9, 2024

概括

这项研究引入了一个级联框架,以使用领域知识在临床试验中标准化疫苗数据. 新模型显著提高了绘制疫苗概念的准确性,提高了研究和公共卫生数据的可用性.

科学领域:

生物信息学是一种生物信息学.
计算生物学计算生物学
医疗信息学医疗信息学

背景情况:

疫苗对公共卫生至关重要,通过刺激免疫记忆来预防传染病.
临床试验对于评估疫苗的有效性,安全性,剂量和管理至关重要.
在临床试验中标准化疫苗数据具有挑战性,但对于知识发展和监测至关重要.

研究的目的:

从临床试验数据开发一个级联框架来自动绘制疫苗本体学 (VO).
增强特定领域的语言模型,以改善疫苗概念标准化和知识提取.
解决疫苗数据库中缺乏标准化的问题.

主要方法:

开发了一个级联框架,集成临床试验,统一医学语言系统 (UMLS) 和疫苗本体学 (VO).
使用了像PubMedBERT和SAPBERT这样的预训练模型,创建了CTPubMedBERT和CTPubMedBERT+SAPBERT.
精心调整的模型与VO体和临床试验数据,采用加权的基于规则的组合进行正常化.
实施了概念规范化排名流程,以确定最合适的匹配.

主要成果:

级联框架在临床试验中绘制VO的现有基线中表现明显优于现有基线.
在前1个概念映射中获得了71.8%的准确性,在前10个概念中获得了90.0%的准确性.
证明了整合多个领域知识来源以提高语言模型性能的有效性.

结论:

开发的级联框架有效地改进了从临床试验数据中绘制疫苗本体学的映射.
利用特定领域的信息和组合方法可以提高自动化疫苗概念标准化.
这种方法为改善疫苗数据的整合和利用提供了一个强大的解决方案.

关键词:

临床试验中的临床试验.域特定的语言模型语言模型规范化规范化规范化疫苗的本体学疫苗的本体学

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