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Digital Health- Dozentinnen/Dozenten
- Prof. Dr. Oliver Amft, Dr. rer. nat. Luis Ignacio Lopera Gonzalez
- Angaben
- Vorlesung mit Übung
Online
4 SWS, benoteter Schein, Anwesenheitspflicht, ECTS-Studium, ECTS-Credits: 5, Sprache Englisch, First lecture: Wednesday, October 20. Online via Zoom and videos via StudOn. Grading: Oral exam (online, 30 min).
Zeit: Mi, Do 14:15 - 15:45, Zoom-Webinar; Bemerkung zu Zeit und Ort: Lectures: Wednesdays 14:15-15:45; Exercises: Thursdays 14:15-15:45.
- Voraussetzungen / Organisatorisches
- First meeting/Vorbesprechung via Zoom: 19.10.2021, 16:00-17:30 h, https://fau.zoom.us/j/63638156157?pwd=TGdVcnVzdWxUYy82MmlnVnVTZmJXdz09
- ECTS-Informationen:
- Credits: 5
- Prerequisites
- Fields of study:
Master Medical Engineering BDV, M5
Master Medical Engineering HMDA, M5
Master Informatik, Mustererkennung
Organisation and slides via StudOn.
- Contents
- 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 goals and competences:
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.
- Literature
- Up-to-date literature recommendations are provided during the lectures.
- Zusätzliche Informationen
- Erwartete Teilnehmerzahl: 20, Maximale Teilnehmerzahl: 20
www: https://www.cdh.med.fau.de/2021/07/08/course-digital-health-2/
Für diese Lehrveranstaltung ist eine Anmeldung erforderlich.
Die Anmeldung erfolgt von Mittwoch, 15.9.2021, 08:00 Uhr bis Freitag, 29.10.2021, 18:00 Uhr über: StudOn.
- Verwendung in folgenden UnivIS-Modulen
- Startsemester WS 2021/2022:
- Digital Health (Digital Health)
- Institution: Lehrstuhl für Digital Health
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