Data-driven Healthcare

4 credits

The course is part of the programme MAISTR ( where participants can take the entire programme or individual courses. The course is for professionals and is held online in English. Application is open as long as there is a possibility of admission. The courses qualify for credits and are free of charge for participants who are citizens of any EU or EEA country, or Switzerland, or are permanent residents in Sweden. More information can be found at

About the course Datadriven Healthcare, 4 credits

Who is this course for?
This course is designed for students with a background in computer science who want to gain additional skills in applying data analytics techniques in the healthcare domain.

What will you learn from this course?
You will learn the concepts and techniques for framing the healthcare problems using a data-driven approach and gain practice analyzing a set of real-world healthcare-related data using machine learning methods.
This course aims to provide a broad introduction to health care analytics: Applying data analytics tools and techniques to organize and analyze healthcare data.

The course is broken down into four parts:

1. Healthcare data understanding and ethics. This part discusses general issues related to the collection, sharing, and management of healthcare data, as well as issues related to patients’ privacy, ethics, bias, social and economic constraints when using healthcare data.

2. Data preparation and visualization. This part will discuss challenges related to healthcare data such as the data size and the class imbalance problem. Then, it introduces techniques for preprocessing healthcare data, extracting and selecting the most relevant features, and visualizing the data.

3. Classification techniques in healthcare data. This part will discuss predictive modeling techniques such as classification using decision trees, neural networks, and others. These techniques will be applied to various practical health care problems, such as: readmission risk assessment, personalization of treatment regimen, predicting patient survival rates, etc.

4. Evaluation metrics in predictive analytics. This part will present commonly used metrics to evaluate the predicted outcomes, but also introduce evaluation strategies relevant in the healthcare domain such as: AB Testing, Propensity Scores, and Randomized Control Trials.

Spring 2022 (Distance (Internet), Varied, 33%)


Advanced level

Application code:


Entry requirements:

Degree of Bachelor in Computer science or Degree of Bachelor of Science in Engineering or the equivalent of 180 Swedish credit points or 180 ECTS credits at an accredited university. Programming 7.5 credits, and Machine Learning 7.5 credits or equivalent. Applicants must have written and verbal command of the English language equivalent to English course 6 in Swedish Upper-Secondary School.

Selection rules:

Credits: 100%

Start week:

week: 03

Number of gatherings:


Instructional time:

Various times

Language of instruction:

Teaching is in English.

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