论文标题
信任问题：不确定性估计无法在医疗表格数据上可靠检测可靠的OOD检测
Trust Issues: Uncertainty Estimation Does Not Enable Reliable OOD Detection On Medical Tabular Data
论文作者
论文摘要
当在高风险现实世界中（例如医疗保健）中部署机器学习模型时，至关重要的是要准确评估有关模型对异常投入的预测的不确定性。但是，文献缺乏在医学数据上分析此问题的文献，尤其是在混合型表格数据（例如电子健康记录）上。我们通过提出一系列测试来缩小这一差距，包括各种当代不确定性估计技术，以确定他们是否能够识别分布外（OOD）患者。与以前的工作相反，我们设计了对现实和临床相关的OOD组的测试，并在现实世界中进行实验。我们发现，几乎所有技术都无法实现令人信服的结果，部分不同意早期的发现。
When deploying machine learning models in high-stakes real-world environments such as health care, it is crucial to accurately assess the uncertainty concerning a model's prediction on abnormal inputs. However, there is a scarcity of literature analyzing this problem on medical data, especially on mixed-type tabular data such as Electronic Health Records. We close this gap by presenting a series of tests including a large variety of contemporary uncertainty estimation techniques, in order to determine whether they are able to identify out-of-distribution (OOD) patients. In contrast to previous work, we design tests on realistic and clinically relevant OOD groups, and run experiments on real-world medical data. We find that almost all techniques fail to achieve convincing results, partly disagreeing with earlier findings.
