Figure 3 | Scientific Reports

Figure 3

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

Figure 3

The CRBM models correlations well. (A) Correlations between variables as predicted by the model (below the diagonal) and calculated from the data (above the diagonal). Components of the cognitive scores are strongly correlated with each other, but not with other clinical data. (B) Scatterplot of observed vs predicted correlations for each time point, over all times. (C) Scatterplot of observed vs predicted autocorrelations with time lag of 3 months. (D) Scatterplot of observed vs predicted autocorrelations with time lag of 6 months. The color gradient in (BD) represents the fraction of observations for which the variables used to compute the correlation were present; lighter colors mean more of the data was missing. In all cases, synthetic patients conditioned on baseline data from actual patients is used (synthetic patients of type (i) above). In (A), the correlation coefficients shown are averaged over the 5 CV models. In (BD), the correlation coefficients for each of the 5 CV folds are shown, and the R2 values shown are the mean and standard deviation over the 5 CV folds, computed from a least squares fit weighted by the fraction of data present when computing the correlations. In all cases the correlations for data are only computed on samples for which the relevant variables are both present (i.e., missing data is ignored).

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