2022 Medical and Health Informatics

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Academic unit or major
Graduate major in Artificial Intelligence
Ohue Masahito 
Class Format
Media-enhanced courses
Day/Period(Room No.)
Tue5-6()  Fri5-6()  
Course number
Academic year
Offered quarter
Syllabus updated
Lecture notes updated
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Course description and aims

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.

Student learning outcomes

- 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.


Medical Informatics, Medical Information System, Precision Medicine, Healthcare ICT

Competencies that will be developed

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/Required learning

  Course schedule Required learning
Class 1 Introduction Understand the outline and aim of the lecture.
Class 2 Medical information system Understand hospital information systems, electronic medical records, and medical information databases.
Class 3 Medical knowledge information processing Understand the knowledge and information handling and language processing in medicine.
Class 4 Healthcare ICT Understand healthcare and medical device development, nursing care robots, and health business related to information technology.
Class 5 Exercise 1 Understand the application of informatics in the field of medical and healthcare informatics through exercises.
Class 6 Medical data modeling 1 Understand how to process X-ray images, MRI images, and cellular images.
Class 7 Medical data modeling 2 Understand how to process and analyze measurement data from electroencephalogram (EEG), electrocardiogram (ECG), and wearable sensors.
Class 8 Exercise 2 Understand actual examples of medical data modeling through exercises.
Class 9 Medical genomics 1 Understand the methods to analyze the relationship between genome and disease.
Class 10 Medical genomics 2 Understand cancer genome analysis and cancer gene panels.
Class 11 Medical information ethics Understand the ethical, legal, and social issues related to genomic medicine and health care.
Class 12 Exercise 3 Understand actual examples of medical genomics through exercises.
Class 13 Survey and reading Understand how to research and read papers in the medical and healthcare fields.
Class 14 Workshop Introduce and discuss papers in fields related to this class.

Out-of-Class Study Time (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.


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.

Assessment criteria and methods

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
  • XCO.T489 : Fundamentals of Artificial Intelligence
  • ART.T458 : Advanced Machine Learning
  • ART.T465 : Sparse Signal Processing and Optimization
  • ART.T551 : Image and Video Recognition

Prerequisites (i.e., required knowledge, skills, courses, etc.)

Programming experience in Python (recommended)


Every student must bring a laptop computer with Python for exercises.

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