Simulation of virtual cohorts increases predictive accuracy of cognitive decline in MCI subjects

TL;DR

This paper introduces a data augmentation method that simulates virtual patient cohorts to improve the accuracy of predicting cognitive decline in MCI subjects, achieving significant error reduction.

Contribution

The study presents a novel simulation framework for generating virtual longitudinal data, enhancing predictive models for cognitive decline in MCI patients.

Findings

37% reduction in mean absolute error with data augmentation

Predictions achieved errors comparable to test/retest data variability

Framework effectively increases data diversity and model robustness

Abstract

The ability to predict the progression of biomarkers, notably in NDD, is limited by the size of the longitudinal data sets, in terms of number of patients, number of visits per patients and total follow-up time. To this end, we introduce a data augmentation technique that is able to reproduce the variability seen in a longitudinal training data set and simulate continuous biomarkers trajectories for any number of virtual patients. Thanks to this simulation framework, we propose to transform the training set into a simulated data set with more patients, more time-points per patient and longer follow-up duration. We illustrate this approach on the prediction of the MMSE of MCI subjects of the ADNI data set. We show that it allows to reach predictions with errors comparable to the noise in the data, estimated in test/retest studies, achieving a improvement of 37% of the mean absolute error compared to the same non-augmented model.