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

Karina Zadorozhny, Patrick Thoral, Paul Elbers, Giovanni Cinà

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review


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.
Original languageEnglish
Title of host publicationStudies in Computational Intelligence
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages17
Publication statusPublished - 2023

Publication series

NameStudies in Computational Intelligence


  • Density estimation
  • Electronic health records
  • Evaluation guidelines
  • Out-Of-Distribution detection

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