MEDICAL APPLICATION AND HEALTH-CARE - MOD. 1
Stampa
Enrollment year
2021/2022
Academic year
2023/2024
Regulations
DM270
Academic discipline
ING-INF/06 (ELECTRONIC AND INFORMATION BIOENGINEERING)
Department
DEPARTMENT OF MATHEMATICS "FELICE CASORATI"
Course
ARTIFICIAL INTELLIGENCE
Curriculum
PERCORSO COMUNE
Year of study
Period
(02/10/2023 - 21/01/2024)
ECTS
3
Lesson hours
24 lesson hours
Language
English
Activity type
WRITTEN AND ORAL TEST
Teacher
DAGLIATI ARIANNA (titolare) - 2 ECTS
CASELLATO CLAUDIA - 1 ECTS
Prerequisites
Basic Knowledge of Machine Learning methods. R programming.
Learning outcomes
In this one-semester course, we will explore a variety of advanced machine learning methods for mining clinical data. Much of this exploration will be a hands-on experience, using time in class to expose the principles of each method.

Through a series of lectures and case studies students will gain an understanding of challenges in using machine learning in health care and its application in medicine. The course will cover key use cases such as clinical decision support, personalized medicine and electronic phenotyping.

During the course we will be using R. R is a state of the art environment for statistical computing, which includes a variety of packages for machine learning.
Learning objectives for the course:
1.Demonstrate familiarity with the literature on advanced data mining methods
2.Present and discuss the application of methods for mining biomedical data
3.Perform analyses of biomedical data using advanced data mining methods and tools
Course contents
Clinical Data (Lessons 1 and 2):
Data generated by health care systems. Type of clinical data, EHR systems, Taxonomies, common biases in clinical data.
Clinical data sharing. Observational Health Data Sciences and Informatics and Federated learning.
Precision Medicine. Omics data and unstructured data for precision medicine. How to embed patient generated data in clinical studies.

Statistical Methods:
How to handle missing data in clinical datasets via Multiple Imputation by Chained Equations (Lesson 3) + LAB 1
Representing time in clinical data: Mixed Effect Models, Sequential pattern mining, Latent Class Mixed Models (Lesson 4). + LAB 2
Survival analysis, Kaplan–Meier estimator and Cox Regression (Lesson 5) + LAB 3

Use Cases:
Electronic phenotyping (Lesson 6) + LAB 4
Clinical studies design (Lesson 7)
Clinical Decision Support Systems (Lesson 8)
Teaching methods
The course is structured with a series of lectures and lboratories, during which the instructors show the application of the presented methodologies presented to real case studies, using the R.
Reccomended or required readings
Slides, recorded lectures, and references available on the Kiro page of the course
Assessment methods
Final Project and Oral Exam
The final project comprises two deliverables- a paper and an in-class presentation. The goal of the project is to demonstrate competency in identifying a public-use biomedical or public health dataset, posing a general research question (NOT a hypothesis), proposing and defending an analytic approach to mining the data in order to address the research question, applying the method, evaluating the results, and proposing new directions for further investigation.
The project can be done by yourself, or working with another student or in a small team. Each student (or team) will present the final project on the last days of class. Papers will be submitted to the instructors at the end of the course.

The Oral Exam will assess the knowldge of the Methods studied during the course.
Further information
Sustainable development goals - Agenda 2030