@inbook{ca859c18a65d48ea9ce1ef470b830578,
title = "Out-of-Distribution Detection for Medical Applications: Guidelines for Practical Evaluation",
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.",
keywords = "Density estimation, Electronic health records, Evaluation guidelines, Out-Of-Distribution detection",
author = "Karina Zadorozhny and Patrick Thoral and Paul Elbers and Giovanni Cin{\`a}",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 2022 Health Intelligence workshop and associated Data Hackathon/Challenge : co-located with the 36th Association for the Advancement of Artificial Intelligence (AAAI) conference, W3PHIAI-22 ; Conference date: 28-02-2022 Through 01-03-2022",
year = "2022",
doi = "https://doi.org/10.1007/978-3-031-14771-5_10",
language = "English",
isbn = "9783031147708",
volume = "1060",
series = "Studies in Computational Intelligence",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "137--153",
editor = "A. Shaban-Nejad and M. Michalowski and S. Bianco",
booktitle = "Studies in Computational Intelligence",
address = "Germany",
}