期刊
KNOWLEDGE-BASED SYSTEMS
卷 213, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.knosys.2020.106688
关键词
Computer-aided diagnosis; Information fusion; Multimodal multitask learning; Alzheimer's disease; Alzheimer's progression; Cognitive scores regression
资金
- Spanish Ministry of Science, Innovation, and Universities [RTI2018-099646-B-I00, TIN2017-84796-C2-1-R, TIN2017-90773-REDT, RED2018-102641-T]
- Galician Ministry of Education, University and Professional Training [ED431F 2018/02, ED431C 2018/29, ED431G/08, ED431G2019/04]
- European Regional Development Fund (ERDF/FEDER program)
- National Research Foundation of Korea (NRF) - Korean government (MSIT) [NRF-2016R1D1A1A03934816]
This study introduces two novel deep learning architectures for AD progression detection, one is an interpretable multitask regression model and the other is a hybrid model, both of which demonstrate effectiveness and practicality on ADNI data.
The prevalence of Alzheimer's disease (AD) in the growing elderly population makes accurately predicting AD progression crucial. Due to AD's complex etiology and pathogenesis, an effective and medically practical solution is a challenging task. In this paper, we developed and evaluated two novel hybrid deep learning architectures for AD progression detection. These models are based on the fusion of multiple deep bidirectional long short-term memory (BiLSTM) models. The first architecture is an interpretable multitask regression model that predicts seven crucial cognitive scores for the patient 2.5 years after their last observations. The predicted scores are used to build an interpretable clinical decision support system based on a glass-box model. This architecture aims to explore the role of multitasking models in producing more stable, robust, and accurate results. The second architecture is a hybrid model where the deep features extracted from the BiLSTM model are used to train multiple machine learning classifiers. The two architectures were comprehensively evaluated using different time series modalities of 1371 subjects participated in the study of the Alzheimer's disease neuroimaging initiative (ADNI). The extensive, real-world experimental results over ADNI data help establish the effectiveness and practicality of the proposed deep learning models. (C) 2020 Elsevier B.V. All rights reserved.
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