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

• URL: http://arxiv.org/abs/2109.14885v1
• Date: Thu, 30 Sep 2021 07:05:20 GMT
• Title: Out-of-Distribution Detection for Medical Applications: Guidelines for Practical Evaluation
• Authors: Karina Zadorozhny, Patrick Thoral, Paul Elbers, Giovanni Cin\`a
• Abstract summary: Out-of-Distribution (OOD) samples in real time is a crucial safety check for deployment of machine learning models in the medical field. There is a lack of evaluation guidelines on how to select OOD detection methods in practice. Here, we propose a series of practical considerations and tests to choose the best OOD detector for a specific medical dataset.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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