Explainable Artificial Intelligence and Process Mining Applications for Healthcare

图书简介

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.

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