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When deploying machine learning models in high-stakes real-world environmentssuch as health care, it is crucial to accurately assess the uncertaintyconcerning a model's prediction on abnormal inputs. However, there is ascarcity of literature analyzing this problem on medical data, especially onmixed-type tabular data such as Electronic Health Records. We close this gap bypresenting a series of tests including a large variety of contemporaryuncertainty estimation techniques, in order to determine whether they are ableto identify out-of-distribution (OOD) patients. In contrast to previous work,we design tests on realistic and clinically relevant OOD groups, and runexperiments on real-world medical data. We find that almost all techniques failto achieve convincing results, partly disagreeing with earlier findings.

Trust Issues: Uncertainty Estimation Does Not Enable Reliable OOD Detection On Medical Tabular Data