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Clinical Applications of Computational Medicine

EI7585
Other subject area

About this course

Computational Medicine is a new scientific field in the intersection between mathematics, physics, biostatistics, computer science, electronics, biomedical engineering and medicine. We focus on actual clinical applications of complex, interdisciplinary solutions for problems in healthcare. Using examples from areas mostly from multiple sclerosis, obstetrics, cardiovascular disease, fall prevention/detection and sport medicine we will explore some of the following important aspects: data collection, biostatistical modelling, filtering, pattern recognition, alarms, prediction, validation, development & certification of web-based tools for clinical decision making.

Learning outcomes

At the end of the module the students are able to understand the problems and key success factors for business models in computational medicine/telemedicine with examples in selected medical areas (multiple sclerosis, obstetrics, cardiovascular disease, fall prevention/detection, exercise therapy). They are able to apply basic signal processing techniques to solve specific problems (filtering and analysis of data from mobile accelerometry/ECG). They also should be able to understand the scientific method to conduct exploratory research generating and testing hypothesis, looking at events, collecting data, analyzing information and reporting the results. In addition, it is expected that they improve their written and oral communications skills by the creation of a scientific report and holding a public presentation.

Examination

After three introductory lectures, the students work in small groups on different projects. Projects are focused on actual clinical applications from some of these areas: multiple sclerosis, obstetrics, cardiovascular disease, fall prevention/detection and sport medicine. The projects will be fixed in detail after the introductory lectures according to the special interests and expertise of the students and the resources. The work done in previous semesters are available at the website of the department to serve as orientation. In general, a project encompasses these tasks: study design, data collection, algorithm development and validation, data analysis and summary of results. Students should prepare a report (maximum 4 pages) including the details of their work as well as a set of slides for the final presentation. The The results will be presented to the audience and defended at the end of the semester. External guests are invited to attend and participate in the final presentation. The quality of the written report, the presentation and the discussion contribute each as 1/3 of the final grade.

Course requirements

Analysis, classical mechanics, fundamentals in electrical engineering, basics in social psychology, basic knowledge of R/Matlab and statistics

Activities

Zunächst wir in einer Reihe von 3 Vorlesungen das Basiswissen zu den einzelnen Themen vermittelt. Darufhin erfolgt eine den Fähigeiten und Interessenslage angepassten Themenauswahl ("matching") zur Vertiefung. Kleingruppen (1-3 Studenten) führen mit entsprechender Unterstützung ein kleines Projekt durch, das am Ende präsentiert und diskutiert wird. Also ist die Lehr- und Lernmethode ein Mix aus Vorlesung, Seminar, Übung und Labor.

Additional information

  • Credits
    ECTS 6
  • Contact hours per week
    2
  • Instructors
    Florian Rattei, Cristina Soaz Gonzalez, Ricarda Baumhoer, Samarjit Chakraborty, Dennis Gölitz, Peter Hausamann, Martin Daumer
  • Mode of instruction
    Hybrid
If anything remains unclear, please check the FAQ of TUM (Germany).

Offering(s)

  • Start date

    17 October 2024

    • Ends
      6 February 2025
    • Term *
      Winter 2024/2025
    • Instruction language
    • Register between
      24 May - 29 Jul 2024
    Only 7 days to enrol
    Apply now
These offerings are valid for students of DTU (Denmark)