医疗保健数据挖掘的伦理:挑战,框架和未来的方向
Mohamed Mustaf Ahmed1, Olalekan John Okesanya2,3, Majd Oweidat4
1Faculty of Medicine and Health Sciences, SIMAD University, Mogadishu, Somalia. momustafahmed@simad.edu.so.
BioData mining
|July 11, 2025
概括
医疗保健数据挖掘提供了洞察力,但也带来了伦理挑战. 实施强有力的透明度,隐私和安全控制对于保护患者权利和信任至关重要.
科学领域:
- 医疗保健数据挖掘是医疗保健的数据挖掘.
- 人工智能伦理学
- 医疗信息学 医疗信息学
- 数据治理数据治理
背景情况:
- 医疗保健数据挖掘提供了有价值的见解,但也引发了重大的伦理和治理问题.
- 患者的隐私和同意至关重要,特别是与数字平台的数据共享,面临违规和未经授权访问的风险.
- 人工智能模型中的算法偏见可能会延续健康差异,需要透明度和问责制.
研究的目的:
- 解决医疗数据挖掘的多方面的伦理和治理挑战.
- 为公平,隐私和安全控制提出一个综合框架.
- 确保负责任的数据挖掘实践,维护患者权利并保持公众信任.
主要方法:
- 综合关于重新识别风险和临床审计结果的证据.
- 建议采用三级透明度方法:数据集文档,模型可解释性和审计日志.
- 建议采取技术保障措施,如差异隐私,同态加密和联合学习.
- 倡导治理框架要求偏见和安全审计.
主要成果:
- 2023年的数据挖掘事件暴露了超过1.33亿个患者记录,自2018年以来,与黑客相关的违规事件增加了239%.
- 2019年的一项研究显示,15个准标识符的个体具有99.98%的独特性.
- 临床审计表明,通过值重新校准,在减少虚假阴性率方面有所改善.
结论:
- 结合数据集"数据表",模型卡和连续日志的综合方法是必不可少的.
- 技术保障和强有力的治理框架,明确的处罚对于问责至关重要.
- 为了实施有效的控制,建议遵守SPIRIT-AI,CONSORT-AI和PROBAST-AI等指南.
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