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

The Artificial Intelligence in Medicine (AIME) society was established in 1986 with the main goals of fostering fundamental and applied research in the application of Artificial Intelligence (AI) techniques to medical care and medical research and providing a forum for discussing any progress made. For this purpose, a series of AIME conferences have been organized on a biennial basis since 1987.The 21st edition of the AIME conference was held in Portoroz, Slovenia, in June 2023. Five workshops were organized in conjunction with the AIME 2023 main conference. This volume contains a selection of the best papers presented at the 3rd International Workshop on eXplainable Artificial Intelligence in Healthcare (XAI-Healthcare 2023) and the 1st InternationalWorkshop on ProcessMiningApplications forHealthcare (PM4H 2023). Preface Organization Contents International Workshop on Explainable Artificial Intelligence in Healthcare Unlocking the Power of Explainability in Ranking Systems: A Visual Analytics Approach with XAI Techniques 1 Introduction 2 Related Work 3 Methodology 3.1 XAI 3.2 Interactive Visualization 4 Case Study: Explaining Triaging Patients to Be Admitted to ICU 5 Discussion 6 Conclusion References Explainable Artificial Intelligence in Response to the Failures of Musculoskeletal Disorder Rehabilitation 1 Introduction 2 Background and Context of This Work 3 Complexity of the Generation of Self-recovery Exercises 3.1 Defining the Rules 3.2 Application of the Consensus Rules 4 General Structure of Recov’Up 5 Explicability 6 Data Extension and Perspectives References An Explainable AI Framework for Treatment Failure Model for Oncology Patients 1 Introduction 2 Scope of Work 2.1 Approaches to Explainability 3 Methodology 3.1 Treatment Failure Explanations 3.2 Model Level Explanations 3.3 Challenges and Limitations 4 Results and Discussion 4.1 Dataset Insights 4.2 Treatment Failure Explanations 4.3 Counterfactual 4.4 Model Level Explanations 5 Conclusion and Future Work References Feature Selection in Bipolar Disorder Episode Classification Using Cost-Constrained Methods 1 Introduction 2 Methodology 2.1 Data Preprocessing 2.2 Algorithm 3 Preliminary Results 4 Conclusions and Future Plans References ProbExplainer: A Library for Unified Explainability of Probabilistic Models and an Application in Interneuron Classification 1 Introduction 2 Background 2.1 Probabilistic Models: Bayesian Networks 2.2 Existing Software 2.3 Interneuron Classification: The Gardener Approach 3 Software Framework 3.1 A Unified Interface 3.2 Design of the Algorithms 4 Application in GABAergic Interneuron Classification 4.1 Data 4.2 Experiments 4.3 Results 5 Conclusions and Future Work References Interpreting Machine Learning Models for Survival Analysis: A Study of Cutaneous Melanoma Using the SEER Database 1 Introduction 2 Surveillance, Epidemiology, and End Results Database 2.1 Selection of the Individuals 2.2 Exploratory Data Analysis 3 Machine Learning Models 3.1 Data Preprocessing 3.2 Machine Learning Models 4 Explainability 5 Conclusions References Explanations of Symbolic Reasoning to Effect Patient Persuasion and Education 1 Introduction 2 Derivation Proofs 3 Human-Readable Explanation Generation from Derivation Proofs 3.1 Pre-processing Module 3.2 Describe Module 3.3 Collect Module 4 Demonstration 5 Planned Evaluation 6 Conclusions and Future Work References International Workshop on Process Mining Applications for Healthcare PMApp: An Interactive Process Mining Toolkit for Building Healthcare Dashboards 1 Introduction 2 Through an Interactive Process Mining Solution for Healthcare 3 PMApp: An Interactive Process Mining Toolkit 3.1 Experiment Designer 3.2 Ingestor Editor 3.3 Dashboard 4 Discussion and Conclusions References A Data-Driven Framework for Improving Clinical Managements of Severe Paralytic Ileus in ICU: From Path Discovery, Model Generation to Validation 1 Introduction 2 Method 2.1 Data Resource 2.2 Cohort Extraction 2.3 Event Log Extraction 2.4 Frequent Patient Pathways Discovery 2.5 Structural Equation Modelling 3 Results 4 Discussion and Conclusion References Phenotypes vs Processes: Understanding the Progression of Complications in Type 2 Diabetes. A Case Study 1 Introduction 2 Methodology 2.1 Data Collection and Study Measures 2.2 Methods 2.3 Data Corpus and Event Log Generation 3 Results 3.1 Patient-Level Analysis 3.2 Short-Term Pathways Analysis 4 Discussion References From Script to Application. A bupaR Integration into PMApp for Interactive Process Mining Research 1 Introduction 2 Background 3 Use Case and Test Scenario 3.1 Filtering Cases Based on Activities and Timestamps 3.2 Data Augmentation and Conditional Filtering 3.3 Manipulation of Activities 4 Discussion and Future Work References Understanding Prostate Cancer Care Process Using Process Mining: A Case Study 1 Introduction 2 Materials and Methods 3 Results 4 Discussion and Conclusions References Author Index 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.