Dealing with heterogeneity in the prediction of clinical diagnosis
Computer assisted diagnosis has emerged as a popular area of research at the intersection of medical imaging and machine learning. Medical data are very heterogeneous in nature and therefore require careful attention when one wants to train prediction models. In this thesis, I explored two sources of heterogeneity, multisite aggregation and clinical label heterogeneity, in an application of magnetic resonance imaging to the diagnosis of Alzheimer’s disease. In the process, I learned about the feasibility of multisite data aggregation and how to leverage that heterogeneity in order to improve generalizability of prediction models. Part one of the document is a general context introduction to Alzheimer’s disease, magnetic resonance imaging, and machine learning challenges in medical imaging. In part two, I present my research through three articles (two published and one in preparation). Finally, part three provides a discussion of my contributions and hints to possible future developments. The first article shows that data aggregation across multiple acquisition sites incurs some loss, compared to single site analysis, that tends to diminish as the sample size increase. These results were obtained through semisynthetic Monte-Carlo simulations based on real data. The second article investigates the generalizability of prediction models with various cross-validation schemes. I showed that training and testing on the same batch of sites over-estimates the accuracy of the model, compared to testing on unseen sites. However, I also showed that training on a large number of sites improves the accuracy on unseen sites. The third article, on clinical label heterogeneity, proposes a new framework where we can identify a subgroup of individuals that share a homogeneous signature highly predictive of AD dementia. That signature could also be found in patients with mild symptoms, 90% of whom progressed to dementia within three years. The thesis thus makes new contributions to dealing with heterogeneity in medical diagnostic applications and proposes ways to leverage that heterogeneity to our benefit.
Machine-learning Alzheimer Early prediction Heterogeneity Multisite aggregation Multisite