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Machine learning models offer the potential to understand diverse datasets ina data-driven way, powering insights into individual disease experiences andensuring equitable healthcare. In this study, we explore Bayesian inference forcharacterising symptom sequences, and the associated modelling challenges. Weadapted the Mallows model to account for partial rankings and right-censoreddata, employing custom MCMC fitting. Our evaluation, encompassing syntheticdata and a primary progressive aphasia dataset, highlights the model's efficacyin revealing mean orderings and estimating ranking variance. This holds thepotential to enhance clinical comprehension of symptom occurrence. However, ourwork encounters limitations concerning model scalability and small datasetsizes.

Bayesian inference of a new Mallows model for characterising symptom sequences applied in primary progressive aphasia