多样性和包容性:开放数据的一个隐藏的额外好处
Marie-Laure Charpignon1,2, Leo Anthony Celi3,4,5, Marisa Cobanaj6
1Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
PLOS digital health
|July 23, 2024
概括
使用开放式关键护理数据集的研究人员显示,与使用私人数据的研究人员相比,来自低收入和中等收入国家和为少数民族服务的机构的多样性更大. 开放数据倡议促进医疗保健研究人工智能的包容性.
科学领域:
- 医疗保健中的人工智能
- 翻译研究是翻译研究.
- 数据科学数据科学数据科学
背景情况:
- 美国国家卫生研究院从2023年1月开始授权公共科学数据共享.
- 数据访问对于减轻临床模型偏差至关重要.
- 开放式访问数据集提高了研究中的透明度和可重复性.
研究的目的:
- 测试这样一个假设:使用开放式数据集的研究人员比使用私有数据集的研究人员更多样化.
- 为了比较人工智能中的作者多样性,用于使用开放式与私有数据集的重症监护出版物.
- 分析低收入和中等收入国家 (LMIC) 和为少数民族服务的机构 (MSI) 的性别平衡和代表性.
主要方法:
- 使用开放式 (MIMIC,eICU) 与私人重症监护数据集的出版物的比较分析.
- 根据性别,LMIC来源和MSI附属的作者多样性的表征.
- 在公开和私有数据用户群体之间对作者人口统计数据的统计比较.
主要成果:
- 与私人数据集相比,开放的重症监护数据集吸引了较高比例的来自LMIC (10.1%与6.2%) 和MSI (8.6%与5.6%) 的作者.
- 性别参与率类似,尽管在开放访问组略高一些.
- 超过70%的文章至少有一位女性作者,但不到25%的文章的第一或最后一位作者是女性.
结论:
- 开放数据策略大大有助于将代表性不足的群体纳入关键护理研究的人工智能领域.
- 开放数据促进了来自LMIC和MSI的多样性,增强了研究参与的广度.
- 持续存在的性别差距仍然存在,强调需要继续努力促进学术和临床研究中的多样性和包容性.
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