2025 (Current Year) Faculty Courses School of Computing Department of Mathematical and Computing Science Graduate major in Artificial Intelligence
Medical and Health Informatics
- Academic unit or major
- Graduate major in Artificial Intelligence
- Instructor(s)
- Masahito Ohue
- Class Format
- Lecture (HyFlex)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 5-6 Tue / 5-6 Fri
- Class
- -
- Course Code
- ART.T553
- Number of credits
- 200
- Course offered
- 2025
- Offered quarter
- 2Q
- Syllabus updated
- Mar 31, 2025
- Language
- English
Syllabus
Course overview and goals
In this class, students will learn about the applications of informatics in the medical and health care fields. In particular, students will understand data analysis and modeling methods for medical and healthcare data through computational exercises and surveys of the latest research, and learn how real problems in the application domain can be solved.
Course description and aims
- To be able to explain and implement basic medical and healthcare informatics methods.
- To be able to explain each topic of medical and healthcare informatics introduced in this lecture.
- To be able to analyze data using the methods and tools introduced in this lecture.
Keywords
Medical genomics, Medical Data Modelling, Bioinformatics
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
Lectures will be conducted using slide materials. In addition to the classroom lectures, students will be required to do exercises. Students will conduct a research survey and present their findings at the end of the class.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Introduction | Understand the outline and aim of the lecture. |
| Class 2 | Bioinformatics 1 | Understand informatics in the medical and healthcare fields. |
| Class 3 | Bioinformatics 2 | Understand informatics in the field of biology and chemistry. |
| Class 4 | Exercise 1 | Understand the application of informatics in the field of medical, healthcare, and bioinformatics through exercises. |
| Class 5 | Medical genomics 1 | Understand the methods to analyze the relationship between genome and disease. |
| Class 6 | Medical genomics 2 | Understand cancer genome analysis. |
| Class 7 | Exercise 2 | Understand actual examples of medical genomics through exercises. |
| Class 8 | Medical data modeling 1 | Understand how to process X-ray images, MRI images, and cellular images. |
| Class 9 | Medical data modeling 2 | Understand how to process and analyze measurement data from electroencephalogram (EEG) and electrocardiogram (ECG). |
| Class 10 | Exercise 3 | Understand actual examples of medical data modeling through exercises. |
| Class 11 | Survey and reading | Understand how to research and read papers in the medical and healthcare fields. |
| Class 12 | Workshop 1 | Introduce and discuss papers in fields related to this class. |
| Class 13 | Workshop 2 | Introduce and discuss papers in fields related to this class. |
| Class 14 | Workshop 3 | Introduce and discuss papers in fields related to this class. |
Study advice (preparation and review)
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.
Textbook(s)
Materials for the course will be provided.
Reference books, course materials, etc.
Edward H. Shortliffe, James J. Cimino. Biomedical Informatics - Computer Applications in Health Care and Biomedicine (5th edition), Springer Cham, 2021. doi:10.1007/978-3-030-58721-5
Other book information will be given in class as necessary.
Evaluation methods and criteria
Course marks are based on exercises (code and report, 60%) and survey (presentation and Q&A, 40%).
Related courses
- CSC.T353 ： Biological Data Analysis
- ART.T543 ： Bioinformatics
- ART.T545 ： Molecular Simulation
- ART.T546 ： Design Theory in Biological Systems
- ART.T458 ： Advanced Machine Learning
- ART.T465 ： Sparse Signal Processing and Optimization
- ART.T551 ： Image and Video Recognition
- XCO.T489 ： Fundamentals of Artificial Intelligence
- XCO.T490 ： Exercises in Fundamentals of Artificial Intelligence
Prerequisites
Programming experience in Python (recommended, we will conduct exercises using Google Colaboratory.)