Short Course on Modelling and Managing Medical Processes

The course offers a comprehensive introduction to workflow modelling and management for the healthcare sector, emphasising techniques to capture, analyse, and optimise medical processes. Participants will explore state-of-the-art approaches - such as Process Mining, Natural Language Processing (NLP), and Large Language Models (LLMs) - to uncover process inefficiencies and enhance patient outcomes. The curriculum features hands-on laboratory sessions where attendees will implement process mining algorithms using the PM4py library (but no background in Python is required), culminating in practical solutions based on healthcare real-world use cases. By the end of the course, learners will be equipped with the technical and analytical skills needed to drive innovation and improve efficiency in medical process management.

The sessions will be held on Campus Est.

• Session 1: May 19, (Monday), 2pm (~3h), room B1.02 • Session 2: May 21, (Wednesday), 9am (~3h), room B1.02 • Session 3: May 23, (Friday), 9am (lab, ~4h), room B1.10

The course is structured into three sessions: the first two are approximately three hours each, and the last is a four-hour laboratory session. Registration is unnecessary, if interested message [email protected].

Lecturer. Manuel Striani is a researcher at the Department of Science and Technological Innovation at the University of Eastern Piedmont (Italy). He has obtained his Ph.D. in Computer Science from the University of Torino with a dissertation centered on semantic process mining and knowledge-based abstraction frameworks. His research spans machine/deep learning, human-computer interaction, process mining, cultural heritage & digital humanities, and artificial intelligence applications in healthcare and education. He has significantly contributed to numerous national and international research initiatives. Notably, he served as Work Package Leader in the European Horizon 2020 SPICE project (Social Participation and Inclusion through Cultural Engagement), which harnessed AI techniques to promote social inclusion via cultural engagement.