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揭开黑猩猩的面具:在医学表格数据中用于分布外检测的基准

Mohammad Azizmalayeri¹, Ameen Abu-Hanna¹, Giovanni Cinà²

¹Department of Medical Informatics, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam, the Netherlands.

International journal of medical informatics

|December 21, 2024

概括

机器学习模型在医疗保健中与分布外 (OOD) 数据作斗争. 这项研究将OOD检测方法与医学表格数据进行了基准测试,发现性能随着数据分布的变化而变化.

科学领域:

医疗信息学医疗信息学
机器学习机器学习
数据科学数据科学数据科学

背景情况:

机器学习 (ML) 模型面临的挑战一般化到外分发 (OOD) 数据,影响医疗保健可靠性.
实时OOD检测对于在临床环境中安全部署ML至关重要.
在医学表格数据上OOD检测方法的有效性仍然在很大程度上未被探索.

研究的目的:

在医学表格数据上建立一个可重复的基准来评估OOD检测方法.
在各种医疗数据集和OOD场景中比较广泛的OOD检测技术.

主要方法:

利用了四个大型公共医疗数据集 (eICU,MIMIC-IV) 与各种OOD生成策略.
评估了10种基于密度的和17种后期的OOD检测方法.
使用三个预测模型架构测试的方法:MLP,ResNet和Transformer.

主要成果:

当OD数据明显可分离时 (AUC ~ 0.98),OD检测方法实现了高性能 (AUC > 0.95).
对于微妙的分布变化 (例如基于种族,年龄) 的表现显著降低 (AUC ~ 0.5).
表明数据可分离性和OOD检测效率之间存在相关性,需要进一步调查.

结论:

该基准提供了一个标准化的评估框架,用于在医学表格数据中检测OOD.
当前的OOD检测方法显示出可变的有效性,特别是在微小的数据分布变化时.
未来的研究应该专注于改善对医疗数据微妙变化的OOD检测.

关键词:

一个基准的基准.医疗人工智能医疗人工智能医学表格数据的数据在分销之外的检测检测安全的安全的安全的安全的安全.

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