  Machine learning models offer the potential to understand diverse datasets in
a data-driven way, powering insights into individual disease experiences and
ensuring equitable healthcare. In this study, we explore Bayesian inference for
characterising symptom sequences, and the associated modelling challenges. We
adapted the Mallows model to account for partial rankings and right-censored
data, employing custom MCMC fitting. Our evaluation, encompassing synthetic
data and a primary progressive aphasia dataset, highlights the model's efficacy
in revealing mean orderings and estimating ranking variance. This holds the
potential to enhance clinical comprehension of symptom occurrence. However, our
work encounters limitations concerning model scalability and small dataset
sizes.
