Course syllabus for Machine learning in healthcare

The course syllabus contains changes
See changes

Course syllabus adopted 2024-02-15 by Head of Programme (or corresponding).

Overview

  • Swedish nameMaskininlärning i hälso- och sjukvård
  • CodeDAT635
  • Credits7.5 Credits
  • OwnerMPMED
  • Education cycleSecond-cycle
  • Main field of studyBiomedical engineering
  • DepartmentCOMPUTER SCIENCE AND ENGINEERING
  • GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail

Course round 1

  • Teaching language English
  • Application code 41120
  • Maximum participants80 (at least 10% of the seats are reserved for exchange students)
  • Block schedule
  • Open for exchange studentsYes
  • Only students with the course round in the programme overview.

Credit distribution

0124 Written and oral assignments 4.5 c
Grading: TH		 4.5 c
0224 Examination 3 c
Grading: TH		 3 c
  • 16 Jan 2025 pm J
  • 15 Apr 2025 am J
  • 26 Aug 2025 am J

In programmes

Examiner

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Eligibility

General entry requirements for Master's level (second cycle)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Specific entry requirements

English 6 (or by other approved means with the equivalent proficiency level)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Course specific prerequisites

One course each in programming, calculus, linear algebra and mathematical statistics. We recommend students have taken a course in machine learning, for example SSY340, TDA233. The course assumes that you are comfortable with basic concepts in probability & statistics, including random variables, probability distributions and densities and expectations.

Aim

The aim for this course is to give knowledge and understanding of learning problems in the healthcare domain and machine learning methods to solve them. It should also provide experience with applying these tools in practical problem solving on real-world health data. 

Learning outcomes (after completion of the course the student should be able to)

Knowledge and understanding

- Give an overview of distinguishing factors of machine learning in healthcare as contrasted with other applications domains
- Describe important learning problems in healthcare and give detailed descriptions of methods used to solve them
- Account for common data types encountered in health applications and their sources
- Describe ethical, regulatory and societal considerations related to working with health data

Skills and abilities

- Formalize learning problems based on inference or decision-making tasks in healthcare
- Gather and preprocess health data for use in machine learning algorithms
- Implement and apply machine learning methods for specific problems

Judgement and approach

- Discuss pros and cons of different machine learning methods for the same problem
- Reflect over the fundamental limitations of available data, infrastructure and learning algorithms in solving a particular task
- Critically analyse and discuss research and applications of machine learning in healthcare

Content

The content is structured into several modules, one for each week of the course. We begin by introducing the domain of machine learning in healthcare and common types of medical data, including coding systems and natural language. We will cover methods that use such diverse data to solve a variety of learning tasks at the level of a single patient and at population level. We will study special considerations which should be taken when applying learning algorithms in healthcare, including dealing with uncertainty and interpretability of model outputs. Additionally, we will cover sufficient conditions for drawing causal inferences from historical data. Toward the end of the course, we will discuss implementation challenges in healthcare processes.

Organisation

Modules composed of lectures, guest lectures and hand-in assignments.

Literature

The course will be based on public material such as research papers and ebooks available online, as well as lecture notes. There will be weekly in-class lectures given by an instructor.

Examination including compulsory elements

Hand-in assignments and written exam.

To pass the course (grade 3), the student must obtain at least 40 % of the total points in the hand-in problems and at least 40 % of the total points in the exam assignment. Higher grades require, in addition to the above, that the combined score from the hand-in problems and the exam, weighted by 60 % and 40 % respectively, exceeds 60 % for grade 4 or 80 % for grade 5.

The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers on educational support due to disability.

The course syllabus contains changes

  • Changes to course rounds:
    • 2024-03-27: Block Block A added by Examinator och UBS
      [Course round 1]