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.
CITATION STYLE
Zadorozhny, K., Thoral, P., Elbers, P., & Cinà, G. (2023). Out-of-Distribution Detection for Medical Applications: Guidelines for Practical Evaluation. In Studies in Computational Intelligence (Vol. 1060, pp. 137–153). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-14771-5_10
Mendeley helps you to discover research relevant for your work.