Detecting label noise in longitudinal Alzheimer’s data with explainable artificial intelligence

Detecting label noise in longitudinal Alzheimer’s data with explainable artificial intelligence

Abstract Reliable classification of cognitive states in longitudinal Alzheimer’s Disease (AD) studies is critical for early diagnosis and intervention. However, inconsistencies in diagnostic labeling, arising from subjective assessments, evolving clinical criteria, and measurement variability, intro...

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Main Authors: Paolo Sorino, Angela Lombardi, Domenico Lofù, Tommaso Colafiglio, Antonio Ferrara, Fedelucio Narducci, Eugenio Di Sciascio, Tommaso Di Noia
Format: Article
Language:English
Published: SpringerOpen 2025-06-01
Series:Brain Informatics
Subjects:
Alzheimer’s disease
Explainable artificial intelligence
SHAP
Noisy labels
Machine learning
Longitudinal studies
Online Access:https://doi.org/10.1186/s40708-025-00261-2
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https://doi.org/10.1186/s40708-025-00261-2

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