ENCoDE - - 一个皮肤色调和临床数据集,来自对急性护理患者的前性试验
medRxiv : the preprint server for health sciences
|August 30, 2024
概括
这项研究创建了一个独特的数据集,将皮肤色调测量与电子健康记录联系起来,以解决脉冲氧计差异. 这些数据可以帮助开发公平的人工智能模型,以实现更公平的医疗保健.
科学领域:
- 医疗信息学 医疗信息学
- 生物医学工程 生物医学工程
- 健康差距 研究 研究 研究 研究
背景情况:
- 皮肤色调,而不是种族,被假设是脉冲氧度差异的原因.
- 以前的研究使用了自我报告的种族作为皮肤色调的代理,限制了准确性.
- 需要与电子健康记录 (EHR) 相关的客观皮肤色调数据来研究健康差异.
研究的目的:
- 创建与EHR数据相关的皮肤色调测量的新型基线数据集.
- 促进与脉冲氧计相关的健康差异研究.
- 为了使医疗保健中更公平的人工智能模型的发展.
主要方法:
- 采集了128名患者在16个身体部位的皮肤色调数据,使用各种设备 (视觉尺度,色度测量,光谱测量,移动摄影).
- 链接皮肤色调数据与电子健康记录信息,包括实验室结果,生命体征和人口统计数据.
- 使用杜克的保护分析计算环境 (PACE) 进行数据链接,常用数据模型转换和非识别.
主要成果:
- 收集了128名患者的全面皮肤色调数据,评估了每个位置的167个特征.
- 在受控条件下获取了2000多张手机图像.
- 成功地将详细的皮肤色调测量与广泛的EHR数据联系起来.
结论:
- 将多个身体部位的详细皮肤色调测量与EHR数据联系起来,可以提高AI模型的公平性.
- 一个共同的数据模型有助于数据共享和多中心研究医疗保健中的皮肤色调.
- 该数据库为解决与皮肤色调相关的医疗保健差异提供了基础,特别是脉冲氧计.
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