提高医疗机器学习潜在偏差的认识:来自数据马拉松的经验
Harry Hochheiser1, Jesse Klug2, Thomas Mathie3
1Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA USA.
medRxiv : the preprint server for health sciences
|November 6, 2024
概括
数据马拉松有效地揭示了医疗机器学习模型中的偏见. 跨学科团队调查了全球开源疾病严重性评分,确定代表性和绩效差异是关键问题.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习 机器学习
- 医疗保健分析 医疗保健分析
背景情况:
- 医疗机器学习模型可能包含偏见.
- 调查这些偏见对于公平的医疗保健至关重要.
- 开源工具为偏见检测提供了机会.
研究的目的:
- 挑战临床医生和信息专家,以识别医学机器学习中的偏见.
- 调查疾病严重性评分的开源严重性评分中的潜在偏见来源.
- 促进对数据和预测偏差的学习.
主要方法:
- 进行了为期两天的数据马拉松.
- 跨学科团队 (临床医生和计算机专家) 参与.
- 团队使用选定的工具来调查全球开源疾病严重性得分 (GOSSIS-1).
主要成果:
- 五个团队参与了,其中大多数使用Python进行分析.
- 共同的主题包括与人口统计学,结果和护理背景的得分关系.
- 确定了潜在的偏见:人口代表性,校准差异和跨设置的性能变化.
结论:
- 数据马拉松对于探索医疗机器学习中未被识别的偏见是有效的.
- 这种方法可以挑战开发人员和用户解决算法公平性.
- 对已发现的偏见进行进一步调查是有必要的.
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