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arXiv · 2109.14885

Out-of-Distribution Detection for Medical Applications: Guidelines for Practical Evaluation

Karina Zadorozhny·Patrick Thoral·Paul Elbers·Giovanni Cinà
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Abstract

Detection of Out-of-Distribution (OOD) samples in real time is a crucial safety check for deployment of machine learning models in the medical field. Despite a growing number of uncertainty quantification techniques, there is a lack of evaluation guidelines on how to select OOD detection methods in practice. This gap impedes implementation of OOD detection methods for real-world applications. Here, we propose a series of practical considerations and tests to choose the best OOD detector for a specific medical dataset. These guidelines are illustrated on a real-life use case of Electronic Health Records (EHR). Our results can serve as a guide for implementation of OOD detection methods in clinical practice, mitigating risks associated with the use of machine learning models in healthcare.

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Karina Zadorozhny, Patrick Thoral, Paul Elbers, Giovanni Cinà. 2021-09-30. Out-of-Distribution Detection for Medical Applications: Guidelines for Practical Evaluation. https://arxiv.org/abs/2109.14885

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