Abstract
Alzheimer’s disease (AD) is a chronic neurodegenerative disorder. Early prediction of Alzheimer’s progression is a crucial process for the patients and their families. As a chronic disease, AD data are multimodal and time series in nature. Building a deep learning model to optimize multi-objective cost function produces a more stable and accurate model. In this paper, we propose a multimodal multitask deep learning model for AD progression detection based five time series modalities and a collection of static data. The model predicts AD progression as a multi-class classification task and four critical cognitive scores as regression tasks. The experimental results show that our model is medically intuitive, more accurate, and more stable than the state-of-the-art studies.
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Acknowledgment
This work was supported by National Research Foundation of Korea-Grant funded by the Korean Government (Ministry of Science and ICT)-NRF-2017R1A2B2012337.
This research was funded by the Galician Ministry of Education, University and Professional Training and by the European Regional Development Fund (ERDF/FEDER program) under grants ED431C 2018/29 and ED431G 2019/04.
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El-Sappagh, S., Abuhmed, T., Kwak, K.S. (2021). Alzheimer Disease Prediction Model Based on Decision Fusion of CNN-BiLSTM Deep Neural Networks. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2020. Advances in Intelligent Systems and Computing, vol 1252. Springer, Cham. https://doi.org/10.1007/978-3-030-55190-2_36
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