Title: Improving the explainability of machine learning techniques in the healthcare domain
Speaker: Jorge Gaete Villegas
Abstract:

The critical nature of medical tasks makes explainability an essential quality for any support system in healthcare. Despite various techniques to provide explainable ML models, challenges still exist. Issues such as model selection, interpretation of results or user interaction with the models are important areas of research to achieve more understandable models and improve their adoption. In this talk, I will present our approach to tackle some of these issues, our current work and its application to patients in the Intensive Care Unit and plans for the rest of my PhD. Special attention will be paid to the use of community detection as an alternative to traditional clustering, and to the potential of leveraging our current results by using Probabilistic Logic Programming to create explainable predictive models.