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Figure 1

From: Machine learning for comprehensive forecasting of Alzheimer’s Disease progression

Figure 1

Overview of the data and model. (A) Study data built from the CAMD database consists of 18-month longitudinal trajectories of 1909 patients with MCI or AD. Our model uses 44 variables, including the individual components of the ADAS-Cog and MMSE scores, laboratory tests, and background information. (B) To capture time dependence, we model the joint distribution of the data at time t + 1 and the data at time t using a Conditional Restricted Boltzmann Machine (CRBM) with ReLU hidden units. Multimodal observations are modeled with different types of units in the visible layer and missing observations are automatically imputed.

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