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

• URL: http://arxiv.org/abs/2011.03274v1
• Date: Fri, 6 Nov 2020 10:41:39 GMT
• Title: Trust Issues: Uncertainty Estimation Does Not Enable Reliable OOD Detection On Medical Tabular Data
• Authors: Dennis Ulmer, Lotta Meijerink and Giovanni Cin\`a
• Abstract summary: We present a series of tests including a large variety of contemporary uncertainty estimation techniques. In contrast to previous work, we design tests on realistic and clinically relevant OOD groups, and run experiments on real-world medical data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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