Explainable Artificial Intelligence and Process Mining Applications for Healthcare

This book constitutes the proceedings of the Third International Workshop on Explainable Artificial Intelligence in Healthcare, XAI-Healthcare 2023, and the First International Workshop on Process Mining Applications for Healthcare, PM4H 2023, which took place in conjunction with AIME 2023 in Portoroz, Slovenia, on June 15, 2023.
The 7 full papers included from XAI-Healthcare were carefully reviewed and selected from 11 submissions. They focus on all aspects of eXplainable Artificial Intelligence (XAI) in the medical and healthcare field. For PM4H 5 papers have been accepted from 17 submissions. They deal with data-driven process analysis techniques in healthcare.

International Workshop on Explainable Artificial Intelligence in Healthcare.- Unlocking the Power of Explainability in Ranking Systems: A Visual Analytics Approach with XAI Techniques.- Explainable Artificial Intelligence Solutions for Musculoskeletal Rehabilitation.- An Explainable AI Framework for Treatment Failure Model for Oncology Patients.- Feature selection in bipolar disorder episode classification using cost-constrained methods.- ProbExplainer: A library for unified explainability of probabilistic models and an application in interneuron classification.- Interpreting Machine Learning models for Survival Analysis: A study of Cutaneous Melanoma using the SEER Database.- Explanations of Symbolic Reasoning to Effect Patient Persuasion and Education.- International Workshop on Process Mining applications for Healthcare.- PMApp: an Interactive Process Mining toolkit for building Healthcare Dashboards.- A data-driven framework for improving clinical management of severe paralytic ileus in ICU: From path discovery, model generation to empirical validation.- Phenotypes vs Processes: supporting the disease progression understanding in Type 2 Diabetes.- From Script to Application. A bupaR-PMApp Bridge for Process Mining Research.- Understanding Prostate Cancer care process using Process Mining: a Case Study.