Course content

The course introduces applying artificial intelligence (AI) methods to structured and textual health data and information.

Learning outcome

• Knowledge. Learn to process and analyze health data and information. Apply AI methods from supervised, semi-supervised, or unsupervised learning to the health data. Understand in what situation these learning approaches are suited, how they work, their advantages and shortcomings, and the type of insight they can provide.
• Skills. Understand the data to be analyzed, how it needs to be preprocessed, and how domain knowledge within should be treated. Can choose suitable AI method(s) to analyze and extract insight from the data. Be able to understand what the AI method learned and how it can support real-world decisions. Can communicate the results to others, both experts and decision-makers.

Learning methods and activities

Lectures and laboratory exercises.

Curriculum, lectures, labs, exercises and exam will be partially given in English, and good English skills are required.

Compulsory assignments

• Exercises and participation in gatherings.

Specific conditions

Recommended previous knowledge

• Basic programming skills in Python
• Statistics
• Clinical data and health records

Review your programming skills with these two online courses:

• Get started with Python, if you have no coding experience: https://www.kaggle.com/learn/intro-to-programming/
• Learn the most important language for data science: https://www.kaggle.com/learn/python/

Required previous knowledge

1. IT6116 Health Data Analysis - Introduction,
2. Three years of education from a college/university, and
3. At least two years of relevant professional practice within health sciences, science and/or informatics.

Other relevant education may provide grounds for education after individual assessment.

Applicants with a foreign educational background must meet the general language requirements in Norwegian and English:

https://www.samordnaopptak.no/info/utenlandsk_utdanning/sprakkrav/krav-til-norsk-og-engelsk-for_hoyere_utdannning/index.html

Documentation requirements:

https://www.ntnu.no/videre/dokumentasjon

Documentation is uploaded when you apply and can be uploaded until the application deadline expires.

We reserve the right to request a sufficient number of applicants for the course to be completed. If there are more applicants than places available, applicants will be prioritized as follows:

1. Employees of The National Archives of Norway and The Norwegian Directorate of Health
2. Applicants residing in Tynset, Alvdal, Folldal, Rendalen, Tolga, Os, Røros, or Holtålen municipalities
3. "First come, first served" principle

Course materials

Suggested textbooks:

• Yu, B., & Barter, R. L. (2024). Veridical data science: The practice of responsible data analysis and decision making. The MIT Press.
• McKinney, W. (2022). Python for data analysis: Data wrangling with pandas, NumPy, and Jupyter (Third edition). O’Reilly.

Supplementary material may be distributed during the course.

Access to your own portable computer is required.