一个关于健康的人工智能研究的文献数据集
Xuanyu Shi1,2, Daoxin Yin2,3, Yongmei Bai1,2
1Institute of Medical Technology, Peking University, Beijing, China.
Health data science
|April 8, 2024
概括
这项研究创建了一个关于健康人工智能 (HAI) 研究的综合数据集,包括出版物和赠款,以分析趋势. 精选的数据遵循FAIR原则,为了解HAI创新提供了宝贵的资源.
科学领域:
- 图书统计学 图书统计学
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
背景情况:
- 健康人工智能 (HAI) 研究正在迅速扩大.
- 景观分析需要一个结构化,全面的数据集.
- 现有的数据来源是分散的,阻碍了整体的研究评估.
研究的目的:
- 为健康人工智能 (HAI) 研究景观分析构建一个精心策划的文献数据集.
- 从多个来源整合各种HAI相关的文献记录.
- 按照 FAIR 原则组织和策划 HAI 文件.
主要方法:
- 在 Medline 和 Dimensions 搜索了与 HAI 相关的出版物,数据集,专利,赠款和临床试验.
- 提取和重新提取的MeSH术语用于文档注释.
- 使用MeSH,FoR,ICD-10和SNOMED CT进行互操作的映射文档.
- 根据预先定义的健康问题和人工智能技术本体学的策划文件.
主要成果:
- 从2009年到2021年收集了96,332份HAI文件.
- 包括出版物 (75,820),数据集 (638),专利 (11,226),赠款 (6,113) 和临床试验 (2,535).
- 在健康问题或人工智能技术方面实现了高标记率 (总体为75.12%,出版物为92.9%).
结论:
- 开发了一个全面的处理和策划HAI文献文档的管道.
- 由此产生的数据集符合FAIR (可查找,可访问,可互操作,可重复使用) 标准.
- 为分析HAI资金,研究和创新提供了一个多维资源.
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