在医学成像和数据资源中心对人口代表性的纵向评估开放数据公众资源
Heather M Whitney1,2, Natalie Baughan1,2, Kyle J Myers2,3
1University of Chicago, Chicago, Illinois, United States.
Journal of medical imaging (Bellingham, Wash.)
|July 20, 2023
概括
医学成像和数据资源中心 (MIDRC) 数据集数据集.
科学领域:
- 医学成像数据科学 数据科学
- 医疗保健中的人工智能
- 公共卫生数据分析
背景情况:
- 医学成像和数据资源中心 (MIDRC) 建立了一个开放的数据共享,以加快人工智能 (AI) 算法的开发,以应对COVID-19大流行.
- 评估这些数据的代表性对于确保人工智能模型的通用性至关重要.
研究的目的:
- 纵向量化主要MIDRC数据集的人口代表性.
- 将MIDRC的人口统计数据与美国一般人口 (美国人口普查) 和COVID-19病例数 (CDC) 进行比较.
主要方法:
- 使用Jensen-Shannon距离 (JSD) 来测量分布相似性.
- 评估了跨年龄,性别,种族和种族的代表性.
- 将MIDRC患者数据与美国人口普查和CDC COVID-19病例数据进行比较.
主要成果:
- 种族和种族/种族组合的代表性受到未报告的CDC数据的影响.
- 根据性别和种族的人口分布随着时间的推移保持了一致的代表性.
- 数据的代表性随着MIDRC扩大其贡献机构和受试者数量而发展.
结论:
- 随着数据共享的增长,MIDRC开放的医学成像数据集的代表性发生了变化.
- 像JSD这样的指标对于测量数据集代表性至关重要.
- 确保公平和可泛化的人工智能算法开发需要持续评估数据的代表性.
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