Abstract
In this paper, we propose a novel deep recurrent neural network as an Alzheimer’s Disease (AD) progression model, capable of jointly conducting tasks of missing values imputation, phenotypic measurements forecast, and clinical state prediction of a subject based on his/her longitudinal imaging biomarkers. Unlike the existing methods that mostly ignore missing values or impute them by means of an independent statistical model before training a disease progression model, we devise a unified recurrent network architecture for jointly performing missing values imputation, biomarker values forecast, and clinical state prediction from the longitudinal data. For these tasks to be handled in a unified framework, we also define an objective function that can be efficiently optimized by means of stochastic gradient descent in an end-to-end manner. We validated the effectiveness of our proposed method by comparing with the comparative methods over the TADPOLE challenge cohort.
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Notes
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\(\left\{ \mathbf{W}_{x},\mathbf{b}_{x},\mathbf{W}_{\gamma },\mathbf{b}_{\gamma },\mathbf{W}_{z}\mathbf{b}_{z},\mathbf{W}_{\beta },\mathbf{b}_{\beta },\mathbf{W}_{h},\mathbf{U}_{h},\mathbf{b}_{h},\mathbf{W}_{y},\mathbf{b}_{y},\mathbf{W}_{f}, \mathbf{b}_{f}\right\} \).
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Acknowledgement
This work was supported by Institute for Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-01779, A machine learning and statistical inference framework for explainable artificial intelligence). According to ADNI’s data use agreement (https://ida.loni.usc.edu/collaboration/access/appLicense.jsp).
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Jung, W., Mulyadi, A.W., Suk, HI. (2019). Unified Modeling of Imputation, Forecasting, and Prediction for AD Progression. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11767. Springer, Cham. https://doi.org/10.1007/978-3-030-32251-9_19
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DOI: https://doi.org/10.1007/978-3-030-32251-9_19
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