Predicting progression of Alzheimer's disease using forward-to-backward bi-directional network with integrative imputation. (June 2022) - Ho, Ngoc-Huynh; Yang, Hyung-Jeong; Kim, Jahae; Dao, Duy-Phuong; Park, Hyuk-Ro; Pant, Sudarshan
Abstract: If left untreated, Alzheimer's disease (AD) is a leading cause of slowly progressive dementia. Therefore, it is critical to detect AD to prevent its progression. In this study, we propose a bidirectional progressive recurrent network with imputation (BiPro) that uses longitudinal data, including patient demographics and biomarkers of magnetic resonance imaging (MRI), to forecast clinical diagnoses and phenotypic measurements at multiple timepoints. To compensate for missing observations in the longitudinal data, we use an imputation module to inspect both temporal and multivariate relations associated with the mean and forward relations inherent in the time series data. To encode the imputed information, we define a modification of the long short-term memory (LSTM) cell by using a progressive module to compute the progression score of each biomarker between the given timepoint and the baseline through a negative exponential function. These features are used for the prediction task. The proposed system is an end-to-end deep recurrent network that can accomplish multiple tasks at the same time, including (1) imputing missing values, (2) forecasting phenotypic measurements, and (3) predicting the clinical status of a patient based on longitudinal data. We experimented on 1, 335 participants from The Alzheimer's Disease Prediction of Longitudinal Evolution (TADPOLE) challenge cohort. The proposed method achieved a mean area under the receiver-operating characteristic curve (mAUC) of 78% for predicting the clinical status of patients, a mean absolute error (MAE) of 3 . 5 m l for forecasting MRI biomarkers, and an MAE of 6 . 9 m l for missing value imputation. The results confirm that our proposed model outperforms prevalent approaches, and can be used to minimize the progression of Alzheimer's disease. Highlights: Deep recurrent network approach was proposed to model long-term Alzheimer's disease. Multi-task learning tackled three problems of missing value imputation, clinical status prediction, and phenotypic measurement forecasting. Integrative imputation combined both statistic and dynamic methods for missing value imputation in longitudinal data. Forward-to-Backward bidirectional strategy captured better temporal dependencies. Multi-feature aggregated LSTM with progressive score estimated biomarker trajectories.