This paper is in the following e-collection/theme issue:

Machine Learning (2134) Clinical Informatics (1353) Evaluation and Research Methodology for mHealth (245) Artificial Intelligence (2202) Development and Evaluation of Research Methods, Instruments and Tools (765)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/78309, first published .
Unsupervised Characterization of Temporal Dataset Shifts as an Early Indicator of AI Performance Variations: Evaluation Study Using the Medical Information Mart for Intensive Care-IV Dataset

Unsupervised Characterization of Temporal Dataset Shifts as an Early Indicator of AI Performance Variations: Evaluation Study Using the Medical Information Mart for Intensive Care-IV Dataset

Unsupervised Characterization of Temporal Dataset Shifts as an Early Indicator of AI Performance Variations: Evaluation Study Using the Medical Information Mart for Intensive Care-IV Dataset

Authors of this article:

David Fernández-Narro1 Author Orcid Image ;   Pablo Ferri1 Author Orcid Image ;   Alba Gutiérrez-Sacristán2 Author Orcid Image ;   Juan M García-Gómez1 Author Orcid Image ;   Carlos Sáez1 Author Orcid Image
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Journals

  1. Nichani K, Uhlig S, San Martin V, Hettwer K, Frost K, Steinacker U, Kaspar H, Gowik P, Kemmlein S. Non-Targeted Screening Method for Detecting Temporal Shifts in Spectral Patterns of Methicillin-Resistant Staphylococcus aureus and Post Hoc Description of Peak Features. Microorganisms 2026;14(1):104 View