A robust framework to investigate the reliability and stability of explainable artificial intelligence markers of Mild Cognitive Impairment and Alzheimer's Disease.
Alzheimer’s disease
Cognitive spectrum
Explainable Artificial Intelligence
Mild Cognitive Impairment
XAI
Journal
Brain informatics
ISSN: 2198-4018
Titre abrégé: Brain Inform
Pays: Germany
ID NLM: 101673751
Informations de publication
Date de publication:
26 Jul 2022
26 Jul 2022
Historique:
received:
12
04
2022
accepted:
03
07
2022
entrez:
26
7
2022
pubmed:
27
7
2022
medline:
27
7
2022
Statut:
epublish
Résumé
In clinical practice, several standardized neuropsychological tests have been designed to assess and monitor the neurocognitive status of patients with neurodegenerative diseases such as Alzheimer's disease. Important research efforts have been devoted so far to the development of multivariate machine learning models that combine the different test indexes to predict the diagnosis and prognosis of cognitive decline with remarkable results. However, less attention has been devoted to the explainability of these models. In this work, we present a robust framework to (i) perform a threefold classification between healthy control subjects, individuals with cognitive impairment, and subjects with dementia using different cognitive indexes and (ii) analyze the variability of the explainability SHAP values associated with the decisions taken by the predictive models. We demonstrate that the SHAP values can accurately characterize how each index affects a patient's cognitive status. Furthermore, we show that a longitudinal analysis of SHAP values can provide effective information on Alzheimer's disease progression.
Identifiants
pubmed: 35882684
doi: 10.1186/s40708-022-00165-5
pii: 10.1186/s40708-022-00165-5
pmc: PMC9325942
doi:
Types de publication
Journal Article
Langues
eng
Pagination
17Subventions
Organisme : Research for Innovation - REFIN funded by Regione Puglia (Italy) in the framework of the POR Puglia FESR FSE 2014-2020 - Asse X - Azione 10.4.
ID : project code 928A7C98
Informations de copyright
© 2022. The Author(s).
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