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FERI:基于多任务的公平性实现算法与公平器官移植的应用

Can Li¹, Dejian Lai¹, Xiaoqian Jiang²

¹Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science

|June 3, 2024

概括

我们开发了通过公平改善率 (FERI) 的公平性算法,以确保对肝移植移植失败的公平预测. 在不影响预测准确性的情况下,FERI提高了患者子组之间的公平性.

科学领域:

医疗信息学医疗信息学
机器学习机器学习
移植医学移植医学

背景情况:

肝移植的结果受到跨人口亚组的公平性挑战的影响.
机器学习模型可以在预测移植结果时引入或加剧偏见.

研究的目的:

引入公平通过公平改善率 (FERI) 算法,以公平预测肝移植患者的移植失败风险.
解决医疗保健中使用的机器学习模型中的偏差.

主要方法:

开发了FERI算法,一种多任务学习方法.
通过平衡学习率和防止子组主导地位,FERI限制了子组损失.
使用AUROC和AUPRC评估预测准确度.

主要成果:

FERI保持了与基线模型相比较的高预测准确度.
在不牺牲准确性的情况下,FERI显著改善了公平度指标.
在性别方面减少了71.74%的人口平等差异,在年龄组中减少了40.46%的人口平等差异.

结论:

FERI算法在医疗保健中推进了公平意识的预测建模.
FERI为开发更公平的医疗保健系统提供了有价值的工具.
移植人工智能的公平性是可以实现的,而不会影响临床效用.

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