Learning of some basic concepts of Computer science.. DUBLIN DESCRIPTORS Knowledge and understanding: with reference to the main topics of the course (coding, relational databases, anonymization of medical data and principles of Atificial Intelligence) The student will be able to understand the key technical terms of computer science; The student will be able to understand simple program codes written by other people; The student will be able to handle issues about storing and retrieving data; The student is able to understand the reading of an article or thesis or scientific book where the principles of artificial intelligence are used for medical applications Apply knowledge and understanding: The student is able to use a programming language to create simple programs. The student is able to create and manage simple databases for clinical or scientific data. Judgment: The student will be able to evaluate the pros and cons of different data organizations. The student will be able to make informed decisions about functionalities of data base management systems. Communication: The student will be able to communicate using an adeguate technical terminology. The student will be able to transfer data using adequate exchanging formats. The student will be able to discuss topics in the field of artificial intelligence applied to medicine. Learning: The student will be able to learn computer science topics concerning data management in medicine. The student will be able to study autonomously AI techniques applied to medicine.

Basic Math and Statistics

For the computer science part: Introduction to the Basic Principles of Medical Informatics. Organization of a computer. The concept of Algorithm. Elements of Artificial Intelligence and Machine Learning for Medicine. Learning vs Fitting. Supervised and Unsupervised Learning. What is Training. Neural Networks and Deep Learning. The concept of generalization. Applications in Medicine of supervised and unsupervised learning. What is Generative AI. The concept of attention. Large Language Models: C. Human and machine learning. Applications in medical research and medical practice. Data storage and manipulation: Spreadsheets versus databases. Introduction to the Basic Principles of Relational Databases. The relational data model. Design, implementation, population, querying of a relational database. Examples in MS Access DB: creation of a database; “Querying by Example”, with particular reference to data aggregation queries for decision support. Other data models: Document DBs and Graph DBs. Anonymization of medical data, and methods of “hacking” databases.

For AI: R. Borhani, S. Borhani, A.K. Katsaggelos, Fundamentals of Machine Learning and Deep Learning in Medicine, Springer, 2022 For Data bases: L. Alluri, U. Nanni, Fondamenti di basi di dati, Hoepli 2021 Lecture notes and other materials, provided by the teaching staff.

ntroduction to the Basic Principles of Medical Informatics. Organization of a computer. The concept of Algorithm. What is a program. Compilers and interpreters. Elements of Artificial Intelligence and Machine Learning for Medicine. Learning vs Fitting. Classical Statistics vs Machine Learning. Supervised and Unsupervised Learning. What is Training. Loss and its Loss Minimization. The concept of overfitting. The concept of Garbage In Garbage Out (GIGO). The artificial neuron. Neural Networks and Deep Learning. The concept of generalization. Convolutional Neural Networks. Applications in Medicine of supervised and unsupervised learning. What is Generative AI. The concept of attention. Large Language Models: ChatGPT (and/or other available tools) and its applications in medicine. Understanding the impact that AI is having and will increasingly have in medicine and in the way patients experience the relationship with doctors. Human learning vs machine learning and other ethical-philosophical issues. Critical commentary on some milestone articles of Machine Learning application in Medicine. Part of data management Information systems in medicine. Introduction to the Basic Principles of Relational Databases. The relational model of data. Design of a relational database. Creation of tables and definition of referential integrity domain key constraints. Population of the database (manual data entry, from file, from query). Examples in MS Access DB “Querying by Example”, with particular reference to data aggregation queries for decision support. Data export to CSV and Excel formats. Exchange formats: CSV, Json, XML. Alternative data models: document model, graph model.

Frontal lesson

The slides and other teaching materials (datasets, exaples of written exams, exercises) can be found in the shared folder in the TEAM channel of the course.

Written test to be taken in the classroom. The computer science part is structured as a quiz composed of 12 questions, some of which are multiple choice and some are free-answer. The sum of the points associated with the questions (declared together with the questions) is 32 which corresponds to grade 30 cum laude. The overall grade of the exam is given by the arithmetic mean of the grades obtained in the statistics test and in the computer science test. Free-answer questions allow you to measure the ability to solve elementary problems. Multiple-choice questions allow you to test your knowledge of the technical language and elementary concepts of the discipline. The grade is the algebraic sum of the scores associated with the individual questions. In multiple-choice questions, the grade of the exercise is assigned when the answer is correct. In free-answer questions, the maximum score expected is assigned when the answer is error-free. In the event of errors in the completion, the corresponding maximum score is reduced based on the severity of the errors. Grade 28-30 cum laude: the student has an IN-DEPTH knowledge of the subject and knows how to solve elementary problems. Grade 24-27: the student has a GOOD knowledge of the subject and a fair ability to solve elementary problems; Grade 18-23: the student has a FAIR knowledge of the subject and a sufficient ability to solve elementary problems. Exam failed: the student is unable to solve elementary problems and has a deficient knowledge.

This course explores topics closely related to one or more goals of the United Nations 2030 Agenda for Sustainable Development (SDGs)
Especially:

● Health and well-being: Computer science can be used to significantly improve the diagnosis and treatment of diseases and decisions related to both. In addition, by consciously using IT tools, learners will be able to provide basic health services to people living in rural or remote areas. Finally, IT tools are crucial to effectively raise awareness of public health problems.
● Quality education: Computer science can be used to improve access to medical and health professions education, make education more personalized and engaging, and provide professional training in the medical and health professions to people who need to find work.
● Gender equality: Computer science can be used to reduce gender inequality in medical education, medical and public health employment, and government.
● Decent work and economic growth: Computer science can be used to
create new medical jobs and new types of medical work, increase productivity in medicine and improve medical working conditions.
● Industry, innovation and infrastructure: Information technology can be used to develop new medical technologies, improve infrastructure efficiency and create jobs in the fields of innovation in the medicna and medical technology.
● Reduction of inequalities: Information technology can be used to reduce inequalities in access to medical services through telemedicine
● Partnership for goals: Computer science can be used to mobilize medical resources, engage collaboration between physicians and professionals from other fields, and share knowledge to achieve the UN SDGs.