不同的模型性能和稳定性在机器学习中 糖尿病和心脏病的临床支持
Ioannis Bilionis1,2, Ricardo C Berrios1, Luis Fernandez-Luque1
1Adhera Health, Santa Cruz, USA.
概括
机器学习 (ML) 模型在慢性疾病预测中显示性别和年龄偏差. 解决模型任意性,而不仅仅是数据表示,对于公平的临床决策至关重要.
科学领域:
- 生物医学信息学 生物医学信息学
- 医疗保健中的人工智能
- 健康 公平 卫生 公平
背景情况:
- 机器学习 (ML) 算法越来越多地用于临床决策.
- 在培训数据中人口群体的代表性不足可能导致绩效差异.
- 现有研究强调了慢性疾病的ML模型中的潜在不平等.
研究的目的:
- 在慢性病数据集和ML模型中调查与性别和年龄相关的不平等.
- 引入一种新的分析框架,以评估超越传统指标的模型任意性.
- 评估数据代表性和模型任意性对预测准确性的影响.
主要方法:
- 分析了来自25,000多个人的慢性疾病数据.
- 应用一种新的框架,将系统的任意性与准确性和数据复杂性指标相结合.
- 评估不同人口群体 (性别和年龄) 的ML模型性能.
主要成果:
- 观察到轻微的与性别相关的差异,男性的预测准确性更高.
- 发现了与年龄相关的显著差异,有利于年轻患者.
- 年龄较大的患者表现出不一致的预测准确性,与更高的数据复杂性和较低的模型性能相关.
结论:
- 仅仅数据的代表性并不能确保医疗保健中的公平ML结果.
- 模型的任意性是导致绩效差异的一个重要因素.
- 在临床部署ML工具之前,解决模型任意性至关重要.
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