基于深度学习和医学文本挖掘的临床试验资格标准的结构分析和智能分类
Yongzhong Han1, Qianmin Su1, Liang Liu2
1School of Electronic and Electric Engineering, Shanghai University of Engineering Science, Shanghai, 201620, China.
Journal of biomedical informatics
|November 27, 2024
概括
本研究引入了临床试验资格标准自动分类 (CTEC-AC) 来构建临床试验标准. 该模型自动化了分类,提高了试验效率和参与者招聘.
科学领域:
- 生物医学信息学 生物医学信息学
- 临床试验管理 临床试验管理
- 自然语言处理自然语言处理.
背景情况:
- 临床试验的效率,质量和创新至关重要.
- 结构化的资格标准对于有效的试验管理至关重要.
- 目前处理资格标准的方法往往是手动的,耗时的.
研究的目的:
- 引入一个新的模型,CTEC-AC,用于对临床试验资格标准的自动分类.
- 将资格标准结构化为可通过计算解释的分类.
- 提高临床试验的效率,质量和创新能力.
主要方法:
- 利用了来自ClinicalTrials.gov的2500个临床试验,产生了超过20,000个符合资格标准的条目.
- 集成的ClinicalBERT和MetaMap,以增强对标准的特征表示.
- 采用等级聚类和专家验证用于自动分类.
主要成果:
- 确定了31个不同的类别,总结了临床试验资格标准.
- 在资格标准的表达中发现了共同的主题.
- 使用自动化分类模型获得了0.94的宏观平均F1得分.
结论:
- CTEC-AC模型自动从非结构化的资格标准文本中提取结构化表示.
- 这一进步显著提高了临床试验的信息化.
- 该模型提高了研究人员自动参与者招募的智能.
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