我可以信任我的假数据吗 - 医疗保健中合成表格数据的综合质量评估框架
Vibeke Binz Vallevik1, Aleksandar Babic2, Serena E Marshall2
1University of Oslo, Boks 1072 Blindern, NO-0316 Oslo, Norway; DNV AS, Veritasveien 1, 1322 Høvik, Norway.
International journal of medical informatics
|March 17, 2024
概括
一个新的框架增强了医疗保健人工智能的合成数据质量保证. 它通过包括公平性和碳足迹来解决隐私问题和监管需求,促进可信的人工智能采用.
科学领域:
- 医疗保健人工智能的人工智能
- 数据科学数据科学数据科学
- 医疗信息学 医疗信息学
背景情况:
- 医疗保健中的AI需要广泛的数据来开发和验证.
- 合成数据为隐私和监管挑战提供了解决方案.
- 目前合成数据质量评估缺乏统一的框架.
研究的目的:
- 开发一个全面的概念框架,以确保医疗保健中合成数据的质量.
- 解决现有的质量评估指标的局限性.
- 促进安全和负责任地采用合成数据.
主要方法:
- 通过深度学习方法生成的表格式医疗保健数据对合成数据质量指标进行文献审查.
- 根据文献和专家经验开发了一个概念框架.
- 基于荷兰国家癌症登记处的案例研究,对框架的适用性进行了基准测试.
主要成果:
- 确定了当前质量评估指标中的差距,重点关注统计相似性,缺乏对公平性和碳足迹的关注.
- 提出了一个概念框架,整合了多种质量维度.
- 强调需要对验证研究和技术标准进行进一步研究.
结论:
- 拟议的框架统一了现有的分类学,并扩大了质量维度,包括公平性和碳足迹.
- 实施该框架可以提高透明度,并减少与合成数据相关的安全风险.
- 对合成数据建立信任对于加速在医疗保健中开发和采用可靠的人工智能工具至关重要.
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