通过临床数据分析用例,探索数据隐私和实用性之间的权衡
Eunyoung Im1,2, Hyeoneui Kim3,4,5, Hyungbok Lee1,6
1College of Nursing, Seoul National University, Seoul, South Korea.
BMC medical informatics and decision making
|May 30, 2024
概括
为了隐私而去识别患者数据可能会损害其分析的有用性. 这项研究表明,平衡数据隐私和实用性是复杂的,需要仔细考虑数据使用和用户输入.
科学领域:
- 医疗信息学 医疗信息学
- 数据 隐私 数据 隐私 数据
- 临床数据分析 临床数据分析
背景情况:
- 数据隐私对于数据利用至关重要,但去识别方法可以减少数据的实用性.
- 平衡数据隐私和实用性是具有挑战性的,对去识别对分析结果的影响的研究有限.
研究的目的:
- 通过使用临床用例来证明非识别方法如何影响数据集实用性.
- 评估实现数据隐私与实用性之间的平衡的可行性.
主要方法:
- 用1155例患者病例的逻辑回归来预测急诊室停留时间的模型.
- 通过使用不同配置的ARX软件生成了19个非识别数据集.
- 在非识别和原始数据集之间比较变量分布和预测结果.
主要成果:
- 所有的19种非识别场景都降低了重新识别的风险.
- 取消识别通过记录抑制和变量掩盖,损害了数据集实用性.
- 只有在重新识别减少率和ARX公用事业分数之间才发现了显著的相关性.
结论:
- 有效的隐私保护对于增加健康数据分析至关重要.
- 为了实现高度的隐私和实用性,需要理解数据的使用和数据用户的参与.
- 协作方法可以帮助在数据隐私和实用性之间找到妥协.
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