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Interpretable machine learning for dementia: A systematic review

Martin, Sophie A; Townend, Florence J; Barkhof, Frederik; Cole, James H; (2023) Interpretable machine learning for dementia: A systematic review. Alzheimer's & Dementia 10.1002/alz.12948. (In press). Green open access

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Abstract

INTRODUCTION: Machine learning research into automated dementia diagnosis is becoming increasingly popular but so far has had limited clinical impact. A key challenge is building robust and generalizable models that generate decisions that can be reliably explained. Some models are designed to be inherently "interpretable," whereas post hoc "explainability" methods can be used for other models. METHODS: Here we sought to summarize the state-of-the-art of interpretable machine learning for dementia. RESULTS: We identified 92 studies using PubMed, Web of Science, and Scopus. Studies demonstrate promising classification performance but vary in their validation procedures and reporting standards and rely heavily on popular data sets. DISCUSSION: Future work should incorporate clinicians to validate explanation methods and make conclusive inferences about dementia-related disease pathology. Critically analyzing model explanations also requires an understanding of the interpretability methods itself. Patient-specific explanations are also required to demonstrate the benefit of interpretable machine learning in clinical practice.

Type: Article
Title: Interpretable machine learning for dementia: A systematic review
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1002/alz.12948
Publisher version: https://doi.org/10.1002/alz.12948
Language: English
Additional information: © 2023 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association. This is an open access article under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/).
Keywords: dementia, diagnosis, explainable artificial intelligence, interpretability, machine learning, mild cognitive impairment
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Brain Repair and Rehabilitation
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10164663
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