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

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Sergio Chayet,Wallace J. Hopp,Xiaowei Xunical 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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Steven R. Applewhite,John M. Gonzalesht 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

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978-3-031-54302-9The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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Conference proceedings 2024 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. .

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Sergio Chayet,Wallace J. Hopp,Xiaowei Xuonent analysis (PCA) was used to identify latent factors. LPM was used to identify structural relationships in the high-frequent process pathways. PLS-SEM was adopted to evaluate the magnitude of relations. Through this framework, the study identified one frequent clinic pathway and six contributing factors for severe PI patients.