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从知情同意文件中提取基于机器学习的捐赠者许可

Meng Zhang¹, Madhuri Sankaranarayanapillai¹, Jingcheng Du¹

¹McWilliam School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.

BMC bioinformatics

|December 15, 2023

概括

这项研究开发了一种自然语言处理 (NLP) 工具,用于在知情同意表格中自动识别关键许可句子. 机器学习模型实现了高精度,有助于生物存储库数据的调节和共享.

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
临床试验管理临床试验管理

背景情况:

临床试验越来越多地涉及为生物银行收集生物样本.
这些试验的知情同意表格变得越来越复杂.
对数据和生物样本共享的同意标准化对研究至关重要.

研究的目的:

开发一种自动化自然语言处理 (NLP) 工具,用于注释知情同意文件.
加强生物库数据监管,共享和决策支持.
识别与生物样本和数据共享许可相关的句子,以及未来的研究.

主要方法:

从公共来源收集知情同意文件.
关于许可的句子手动注释的文件.
评估了机器学习算法,包括随机森林 (RF) 和支持矢量机器 (SVM).

主要成果:

有注释的120份知情同意文件 (29,204个句子).
确定了1250个 (4.28%) 与许可问题相关的句子.
在预先过的语料库上使用SVM模型实现了0.95的F1得分.

结论:

证明了使用机器学习来分类与许可相关的句子的可行性.
NLP工具可以支持生物银行中的监管合规性和数据共享.
自动注释提高了同意表格分析的效率和准确性.

关键词:

经过知情同意的同意.机器学习是机器学习.自然语言处理自然语言处理.文字分类文本分类文本分类

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