Clustering results interpretation of continuous variables using Bayesian inference

Abstract

The present study is devoted to interpretable artificial intelligence in medicine. In our previous work we proposed an approach to clustering results interpretation based on Bayesian Inference. As an application case we used clinical pathways clustering explanation. However, the approach was limited by working for only binary features. In this work, we expand the functionality of the method and adapt it for modelling posterior distributions of continuous features. To solve the task, we apply BEST algorithm to provide Bayesian t-testing and use NUTS algorithm for posterior sampling. The general results of both binary and continuous interpretation provided by the algorithm have been compared with the interpretation of two medical experts. © 2021 European Federation for Medical Informatics (EFMI) and IOS Press.