The model accurately forecasts progression and allows for interpretation. (A) Violin plot of the ADAS-Cog score over time computed from the data and the model. The data from all 5 CV folds are shown together. (B) Out-of-sample predictive accuracy for the change in ADAS-Cog score from baseline (i.e., t = 0) for different study durations. Separate neural network, random forest, and linear regression models were trained to predict the change in ADAS-Cog score from baseline for each study duration. The points (errors) are the means (standard deviations) over the 5 CV folds. (C) We created a simulated patient population with MCI and an initial ADAS-Cog score of 10, and simulated the evolution of each synthetic patient for 18 months. The 5% of synthetic patients with the largest ADAS-Cog score increase were designated “fast progressors” and the bottom 5% of patients with the smallest ADAS-Cog score increase were designated “slow progressors”. Differences between the fast and slow progressors (the “absolute effect size”) were quantified using the absolute value of Cohen’s d-statistic, which measures the mean difference divided by a pooled standard deviation47. The average effect size over the 5 CV folds is shown for each variable.