Machine learning approaches are increasingly being used to identify discriminative features derived from functional connectome data that best separate a diseased group from healthy cohorts. Here, we propose a novel framework for longitudinal prediction of disease outcome, using a combination of unsupervised and supervised learning approaches. Using this framework, we achieve 81% accuracy for prediction of mild traumatic brain injury outcome at 3-months by learning features from functional connectomes at the acute stage of injury (<1 week).
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