Analyzing Medical Data with Process Mining: A COVID-19 Case Study

• Published in: Business Information Systems Workshops pp. 39 - 44
• Main Authors: Pegoraro, Marco, Narayana, Madhavi Bangalore Shankara, Benevento, Elisabetta, van der Aalst, Wil M. P., Martin, Lukas, Marx, Gernot
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing
• Series: Lecture Notes in Business Information Processing
• Subjects: COVID-19; Healthcare; Process mining
• ISBN: 9783031042157; 3031042158
• ISSN: 1865-1348; 1865-1356
• DOI: 10.1007/978-3-031-04216-4_4

The recent increase in the availability of medical data, possible through automation and digitization of medical equipment, has enabled more accurate and complete analysis on patients’ medical data through many branches of data science. In particular, medical records that include timestamps showing the history of a patient have enabled the representation of medical information as sequences of events, effectively allowing to perform process mining analyses. In this paper, we will present some preliminary findings obtained with established process mining techniques in regard of the medical data of patients of the Uniklinik Aachen hospital affected by the recent epidemic of COVID-19. We show that process mining techniques are able to reconstruct a model of the ICU treatments for COVID patients.