Zhang, Y., Liu, T., Lanfranchi, V. orcid.org/0000-0003-3148-2535 et al. (1 more author) (2022) Explainable tensor multi-task ensemble learning based on brain structure variation for Alzheimer's disease dynamic prediction. IEEE Journal of Translational Engineering in Health and Medicine, 11. pp. 1-12. ISSN 2168-2372
Abstract
Objective: Machine learning approaches for predicting Alzheimer’s disease (AD) progression can substantially assist researchers and clinicians in developing effective AD preventive and treatment strategies.
Methods: This study proposes a novel machine learning algorithm to predict the AD progression utilising a multi-task ensemble learning approach. Specifically, we present a novel tensor multi-task learning (MTL) algorithm based on similarity measurement of spatio-temporal variability of brain biomarkers to model AD progression. In this model, the prediction of each patient sample in the tensor is set as one task, where all tasks share a set of latent factors obtained through tensor decomposition. Furthermore, as subjects have continuous records of brain biomarker testing, the model is extended to ensemble the subjects’ temporally continuous prediction results utilising a gradient boosting kernel to find more accurate predictions.
Results: We have conducted extensive experiments utilising data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to evaluate the performance of the proposed algorithm and model. Results demonstrate that the proposed model have superior accuracy and stability in predicting AD progression compared to benchmarks and state-of-the-art multi-task regression methods in terms of the Mini Mental State Examination (MMSE) questionnaire and The Alzheimer’s Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) cognitive scores.
Conclusion: Brain biomarker correlation information can be utilised to identify variations in individual brain structures and the model can be utilised to effectively predict the progression of AD with magnetic resonance imaging (MRI) data and cognitive scores of AD patients at different stages.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ |
Keywords: | Alzheimer’s disease; multi-task learning; brain biomarker spatio-temporal correlation; tensor decomposition; gradient boosting ensemble learning |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 07 Nov 2022 17:27 |
Last Modified: | 25 Sep 2024 15:33 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/JTEHM.2022.3219775 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:192750 |