Artificial Intelligence and Decision Making for Health and Medicine
A.Y. 2024/2025
Learning objectives
The course aims to equip students with knowledge and tools to develop a critical understanding of artificial
intelligence techniques and decision-making processes in healthcare and medicine. Students will gain the capability
to discern the potential applications of these methods for both research and practical purposes.
intelligence techniques and decision-making processes in healthcare and medicine. Students will gain the capability
to discern the potential applications of these methods for both research and practical purposes.
Expected learning outcomes
At the conclusion of the course, students will attain the following learning outcomes:
· Gain insight into the methodological aspects associated with machine learning techniques.
· Establish connections between artificial intelligence and decision making.
· Show a thorough understanding of the differences between supervised and unsupervised learning
techniques.
· Grasping the applications of artificial intelligence and decision making in the domains of health and
medicine
· Gain insight into the methodological aspects associated with machine learning techniques.
· Establish connections between artificial intelligence and decision making.
· Show a thorough understanding of the differences between supervised and unsupervised learning
techniques.
· Grasping the applications of artificial intelligence and decision making in the domains of health and
medicine
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Course syllabus
Principles of decision making. Linear programming. Quadratic and nonlinear optimization. Multiple criteria decision making. Applications in healthcare and medical modeling.
Introduction to artificial intelligence and machine learning. Key definitions and foundational concepts.
Supervised learning and loss minimization. Machine learning algorithms: training and testing. Applications in disease diagnosis and patient monitoring.
Introduction to artificial intelligence and machine learning. Key definitions and foundational concepts.
Supervised learning and loss minimization. Machine learning algorithms: training and testing. Applications in disease diagnosis and patient monitoring.
Prerequisites for admission
Basic knowledge of mathematics and statistics.
Teaching methods
Teaching methods include regular face-to-face lectures, tutorials, homeworks, team working on case-studies and research papers.
Teaching Resources
1. Lecture notes and slides.
2. Pardalos, P.M., A Romeijn, H.E, Handbook of Optimization in Medicine, 2014, Springer.
3. Ben David, S., Curigliano, G., Koff, D., Jereczek-Fossa B., La Torre, D., Pravettoni, G., Artificial Intelligence for Medicine: An Applied Reference for Methods and Applications, 2024, Elsevier.
2. Pardalos, P.M., A Romeijn, H.E, Handbook of Optimization in Medicine, 2014, Springer.
3. Ben David, S., Curigliano, G., Koff, D., Jereczek-Fossa B., La Torre, D., Pravettoni, G., Artificial Intelligence for Medicine: An Applied Reference for Methods and Applications, 2024, Elsevier.
Assessment methods and Criteria
The assessment (for attending students) is based on in-class activties (group presentations) and a final MCQ test. The assessment for non-attending students is only based on an oral exam.
INF/01 - INFORMATICS - University credits: 6
Lessons: 40 hours
Professor:
La Torre Davide
Shifts:
Turno
Professor:
La Torre DavideEducational website(s)
Professor(s)