Titlebook: Explainable Artificial Intelligence and Process Mining Applications for Healthcare; Third International Jose M. Juarez,Carlos Fernandez-Ll

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Feature Selection in?Bipolar Disorder Episode Classification Using Cost-Constrained Methodsents’ behavior. The features in this task are associated with costs. Besides basic (low-cost) information about patients’ phone calls and text messages, we are studying the impact of acoustic features (high-cost) on classifying patients’ states. Unlike in previous papers, now we take the costs into

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Interpreting Machine Learning Models for?Survival Analysis: A Study of?Cutaneous Melanoma Using the?ing the SEER database to model cutaneous malignant melanoma. Additionally, we employ SurvLIMEpy library, a . package designed to provide explainability for survival machine learning models, to analyse feature importance. The results demonstrate that machine learning algorithms outperform the Cox Pro

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Explanations of?Symbolic Reasoning to?Effect Patient Persuasion and?Educationion regimens, dietary habits, physical activity, and avoiding flare-ups. Instead of merely positing an “edict,” the AI model can also explain . the recommendation was issued: why one should stay indoors (e.g., increased risk of flare-ups), why further calorie intake should be avoided (e.g., met the

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PMApp: An Interactive Process Mining Toolkit for?Building Healthcare Dashboardsthe healthcare sector lacks a comprehensive solution addressing flexibility, connectivity, and usability. This paper presents the PMApp, the Interactive Process Mining Toolkit, a specialized tool for healthcare professionals. PMApp is designed to adapt to diverse scenarios, seamlessly integrate with

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A Data-Driven Framework for Improving Clinical Managements of Severe Paralytic Ileus in ICU: From Panical pathways, identifying risk factors on high-frequency pathways may facilitate the efficient optimization of clinical processes. This paper illustrated a data-driven framework that combines local process optimization and conceptual model validation. Frequent clinic pathways and contributing fact

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Phenotypes vs Processes: Understanding the?Progression of?Complications in?Type 2 Diabetes. A?Case Sht glycemic control, both in terms of glycated hemoglobin and the time in range, with different metabolic pathways that contribute to nervous, micro, and macro-vascular complications. As a matter of fact, a few works attempted to obtain phenotyping to describe the evolution of type 2 diabetes patien