  Alzheimer's disease (AD) is known as one of the major causes of dementia and
is characterized by slow progression over several years, with no treatments or
available medicines. In this regard, there have been efforts to identify the
risk of developing AD in its earliest time. While many of the previous works
considered cross-sectional analysis, more recent studies have focused on the
diagnosis and prognosis of AD with longitudinal or time series data in a way of
disease progression modeling (DPM). Under the same problem settings, in this
work, we propose a novel computational framework that can predict the
phenotypic measurements of MRI biomarkers and trajectories of clinical status
along with cognitive scores at multiple future time points. However, in
handling time series data, it generally faces with many unexpected missing
observations. In regard to such an unfavorable situation, we define a secondary
problem of estimating those missing values and tackle it in a systematic way by
taking account of temporal and multivariate relations inherent in time series
data. Concretely, we propose a deep recurrent network that jointly tackles the
four problems of (i) missing value imputation, (ii) phenotypic measurements
forecasting, (iii) trajectory estimation of the cognitive score, and (iv)
clinical status prediction of a subject based on his/her longitudinal imaging
biomarkers. Notably, the learnable model parameters of our network are trained
in an end-to-end manner with our circumspectly defined loss function. In our
experiments over TADPOLE challenge cohort, we measured performance for various
metrics and compared our method to competing methods in the literature.
Exhaustive analyses and ablation studies were also conducted to better confirm
the effectiveness of our method.
