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

BOLT

Adherent

仲裁者

公理

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

调情

灾祸

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

Tidious

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

syring

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

expeditious

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

adumbrate

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