识别和减轻电子表型中的偏见:从计算角度进行全面的研究.
Sirui Ding1, Shenghan Zhang2, Xia Hu3
1Department of Computer Science & Engineering, Texas A&M University, College Station, TX, United States.
Journal of biomedical informatics
|June 14, 2024
概括
这项研究调查了精准医学电子表型学方面的偏见. 它评估了脱皮方法,以确保在临床试验等医疗保健应用中公平地代表患者子组.
科学领域:
- 生物医学信息学 生物医学信息学
- 医疗保健中的机器学习
- 健康 公平 卫生 公平
背景情况:
- 电子表型对精准医学和现实世界的证据生成至关重要.
- 机器学习已经使用电子健康记录进行了先进的电子表型识别.
- 目前的重点是准确性,忽视了公平性,导致患者子组代表性不足.
研究的目的:
- 综合研究电子表型模型中的偏见.
- 评估用于表型定型的脱方法的有效性.
- 解决患者子组识别的公平性考虑方面的差距.
主要方法:
- 基准 9 种电子表型化方法 (基于规则的数据驱动).
- 评估了5种偏差缓解策略 (预处理,在处理,后处理).
- 专注于肺炎和败血症作为偏见识别的目标疾病.
主要成果:
- 在电子表现型模型中确定了特定的偏见.
- 在不同的表型化方法中评估了各种脱皮化技术的性能.
- 提供了对医疗保健中偏见缓解策略有效性的见解.
结论:
- 电子表型模型可以表现出显著的偏见,影响医疗保健公平.
- 偏差缓解策略显示出潜力,但需要根据表型化任务进行仔细选择.
- 需要进行进一步的研究,以确保在数字健康中公平和公平地识别患者子组.
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