Fully Personalised Degenerative Disease Modelling - A Duchenne Muscular Dystrophy Case Study

Abstract Predicting the trajectory of rare degenerative diseases can be extremely beneficial, especially when these predictions are personalised to be relevant for a specific patient. These predictions can help inform and advise patients, families, and clinicians about the next stages of treatment and care. Obtaining such predictions, however, can be challenging, especially when data is limited. In particular, it is important that these predictions do not rely too heavily on general trends from the wider afflicted population while not relying exclusively on the, potentially sparse, data from the patient in question. We present a case study, wherein a modelling framework is developed for predicting a patient’s long term trajectory, using a mix of data from the patient of concern and a database of previously observed patients. This framework directly accounts for the temporal structure of a patient’s trajectory, effortlessly handles a large amount of missing data, allows for a wide range of patient progression, and offers a robust quantification of the various uncertainties. We showcase this framework to an example involving Duchenne Muscular Dystrophy, where it provides promising results.