Bayesian inference of a new Mallows model for characterising symptom
sequences applied in primary progressive aphasia
- URL: http://arxiv.org/abs/2311.13411v1
- Date: Wed, 22 Nov 2023 14:16:20 GMT
- Title: Bayesian inference of a new Mallows model for characterising symptom
sequences applied in primary progressive aphasia
- Authors: Beatrice Taylor and Cameron Shand and Chris J. D. Hardy and Neil
Oxtoby
- Abstract summary: We explore Bayesian inference for characterising symptom sequences.
We adapt the Mallows model to account for partial rankings and right-censored data.
This holds the potential to enhance clinical comprehension of symptom occurrence.
- Score: 0.13499500088995461
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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