Unmasking the chameleons: A benchmark for out-of-distribution detection in medical tabular data.
Journal:
International journal of medical informatics
Published Date:
Dec 17, 2024
Abstract
BACKGROUND: Machine Learning (ML) models often struggle to generalize effectively to data that deviates from the training distribution. This raises significant concerns about the reliability of real-world healthcare systems encountering such inputs known as out-of-distribution (OOD) data. These concerns can be addressed by real-time detection of OOD inputs. While numerous OOD detection approaches have been suggested in other fields - especially in computer vision - it remains unclear whether similar methods effectively address challenges posed by medical tabular data.