Course: Digital Health

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Course description

Digital health is a branch of digital medicine that integrates and leverages multisource and multimodal data for medical knowledge extraction and decision support across a wide range of preventive, diagnostic, and therapeutic applications. The course starts by introducing the basic properties of medically relevant data sources and their different modalities. The course introduces the medical benefits of using ubiquitous technologies for data collection, in particular, between hospital visits. The process of medical data integration in clinical information systems and in digital health applications (“Digitale Gesundheitsanwendungen”, DGA) is discussed. The German DGA regulations and their consequences are introduced, in particular relating to digital health application qualification and data privacy. Privacy preserving techniques are discussed and applied. Subsequently, data interpretation in telemedicine and digital biomarker design are analysed regarding context recognition and personalisation methods and algorithms. Decision support systems are dissected regarding their components and data analysis algorithms. Finally, the concept, realisation, and application of digital health twins in medicine is developed. The exercises will include practical experiments and implementation tasks, e.g. smartphone apps, 3D digital twin modelling, and data analysis for decision support.

Learning objectives

  • Understand the data sources and modalities in digital medicine.
  • Understand the German DGA regulation and issues relating to data privacy.
  • Understand the processes of data integration in clinical information systems and DGAs.
  • Apply ubiquitous technology (ambient, mobile, wearable, implantable) for digital health.
  • Apply context recognition and personalisation methods to qualify ubiquitous system data.
  • Apply data-based privacy preserving techniques (obfuscation) in DGAs (Smartphone apps).
  • Design and implement digital biomarkers based on multimodal data.
  • Design and apply digital health twins.
  • Design medical decision support systems based on multimodal data.

Course data

ECTS
5
Language
English
Presence time
Lecture: 2 SWS, Exercise: 2 SWS
Useful knowledge
Basics of Python. Basics in signal processing
Period
Winter term 2021/22
Presence time
Virtual course, working from remote.
Registration
Obligatory, via StudOn.
First meeting First lecture on October 20, 14:15

Registration via StudOn StudOn – Digital Health (fau.de) obligatory. Please observe the registration times on StudOn.

Literature

Up-to-date literature recommendations are provided during the lectures.

Fields of study

  • Master Medical Engineering BDV, M5
  • Master Medical Engineering HMDA, M5
  • Master Informatik, Mustererkennung

Examination

Written e-exam (60 min.), date will be announced.

Prerequisites / Organisational information

Organisation and material via StudOn.

Contact

Prof. Dr. Oliver Amft

  • Job title: Director
  • Address:
    Henkestr. 91, Geb. 7
    91052 Erlangen
  • Phone number: +49 9131 85-23601
  • Email: oliver.amft@fau.de

Dr. Luis I. Lopera G.

  • Job title: Researcher
  • Address:
    Henkestraße 91, Haus 7, 1. OG
    91052 Erlangen
    Germany
  • Phone number: +49 9131 85-23605
  • Email: luis.i.lopera@fau.de

Friedrich-Alexander-Universität Erlangen-Nürnberg