Vertigo can have many underlying causes and is a common reason for visiting the emergency department. In this project we extend an existing decision tree and random forest (RF) model for classifying patients into high and low probability groups, with CNN model and we will experiment a variety of explainability methods available in literature including those developed by the KDD Lab-CNR-Pisa group within the ERC project XAI. In particular, we will concentrate on post-hoc explanations experimenting methods that are local, global and/or based on medical ontology as Doctor XAI. The existing RF model and the CNN model are developed at UMU. A comparison between RF and CNN will support a better understanding of model accuracy whereas accompanying CNN with XAI methods will give insights on the usability for the medical specialist.

Output

vertigo dataset

conference paper (submitted to e.g. FAccT or IJCAI)

journal paper

Project Partners:

  • UMU, Virginia Dignum
  • UMU, Virginia Dignum
  • CNR-Pisa, Fosca Giannotti

Primary Contact: Virginia Dignum, UMU