EPN-V2

BIOB1060 Evidence-Based Practice (EBP) in Health Care Course description

Course name in Norwegian
Kunnskapsbasert praksis (KBP) i helsetjenesten
Study programme
Biomedical Laboratory Sciences Programme
Weight
5.0 ECTS
Year of study
2025/2026
Curriculum
FALL 2025
Schedule
Course history

Introduction

None

Recommended preliminary courses

Kristoffer H. Hellton

Required preliminary courses

After completing the course, the student is expected to have achieved the following learning outcomes defined in terms of knowledge, skills and general competence.

Knowledge

The student can:

  • explain fundamental concepts in regression analysis such as explanatory variables, response variable, explained variance, prediction intervals and the least squares method
  • know of maximum likelihood estimation, likelihood ratio test, bootstrapping and generalized linear models
  • explain how machine learning methods are trained, validated, and evaluated
  • explain the difference between local and global explanations, and how the explainability methods partial dependence plot, accumulated local effects plot, Friedman's H-statistic, and surrogate models work.

Skills

The student can:

  • use linear and logistic regression to study the relationship between one or more explanatory variables and a response variable
  • fit an appropriate regression model by studying the model's residuals and, if necessary, attempt various variable transformations and variable selection
  • formulate and perform hypotheses tests and calculate confidence intervals for parameters in a regression model
  • identify the most important predictors in a model using explainable artificial intelligence (XAI)
  • write source code to perform calculations for regression analyses and XAI in Python

General competence

The student can:

  • apply statistical thinking to real-world problems and communicate these both in writing and orally
  • solve real-world problems using regression analysis, machine learning and XAI
  • identify the limitations of the methods and assess similarities and differences with methods from mathematical modeling, numerical analysis, and machine learning

Learning outcomes

Lectures and individual exercises. The exercise sessions will consist of both problem-solving and programming.

Teaching and learning methods

The following coursework is mandatory and must be approved to be eligible for the exam:

Three mandatory assignments, of which at least two must be approved. The work is done in groups of 1-4 members.

Course requirements

Individual written exam of 3 hours under supervision.

The exam result can be appealed.

In the event of resit or rescheduled exams, another exam form may also be used. If oral exams are used, the result cannot be appealed.

Assessment

All written and printed aids are allowed. Handheld calculator that cannot be used for wireless communication or to perform symbolic calculations. If the calculator has the capability for internal memory storage, the memory must be cleared before the exam. Random checks may be carried out.

Permitted exam materials and equipment

All aids are permitted, as long as the rules for source referencing are complied with.

Grading scale

Pass/Fail. The same grade is given for all students in the group.

Examiners

One internal examiner. External examiners are used regularly.

Overlapping courses

The course builds on DAPE2000 Mathematics 2000 with Statistics.