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.

通过贝叶斯推理和定制的MCMC拟合，我们适应了Mallows模型来描述症状序列的特征和建模挑战。我们的评估结果包括合成数据和一个主要进行性失语症数据集，突出了该模型在揭示平均顺序和估计排序方差方面的效力，从而有助于增强临床对症状发生的理解。然而，我们的工作面临模型可扩展性和数据集规模的限制。

基于贝叶斯推断的新型Mallows模型在初级进行性失语症状序列表征中的应用