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Explainable Artificial Intelligence and Process Mining Applications for Healthcare

Third International Workshop, XAI-Healthcare 2023, and First International Workshop, PM4H 2023, Portoroz, Slovenia, June 15, 2023, Proceedings
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ISBN-13:
9783031543036
Veröffentl:
2024
Seiten:
131
Autor:
Jose M. Juarez
Serie:
2020, Communications in Computer and Information Science
eBook Typ:
PDF
eBook Format:
EPUB
Kopierschutz:
1 - PDF Watermark
Sprache:
Englisch
Beschreibung:

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.
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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