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Vorlesungsverzeichnis >> Medizinische Fakultät (Med) >>
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COVID-19 – Digital Symptom Tracker- Dozentinnen/Dozenten
- Prof. Dr. Oliver Amft, Dr. rer. nat. Luis Ignacio Lopera Gonzalez
- Angaben
- Seminar
Online
4 SWS, benoteter Schein, Anwesenheitspflicht, ECTS-Studium, ECTS-Credits: 5, Sprache Englisch
Zeit:
Vorbesprechung: 4.11.2020, 16:15 - 17:45 Uhr
- Voraussetzungen / Organisatorisches
- Link to the online introduction/Vorbesprechung: https://fau.zoom.us/j/96876884625?pwd=bm1aZWc4UnYyYllnMFc1bkszVWUrZz09
- ECTS-Informationen:
- Credits: 5
- Prerequisites
- Useful knowledge:
Python, data analytics.
- Contents
- Background:
COVID-19 has forced the world into social distancing and self-isolation, and with the general goal of not saturating the healthcare system, establishing an adequate threshold for when to contact the doctor is difficult. Some people may be overreacting to any minor symptom, while other people might be waiting way too long before getting medical advice. As the lockdown continues, it becomes harder to remember when symptoms first appear and their severity across multiple days.
Aim:
The goal of the project is to create a user-friendly symptom tracking application. The core focus is the UI design. The app should provide an engaging interface to track symptoms and to remind users to take respective measurements, e.g., temperature, and when to contact the house doctor. The app should be knowledge-based so that privacy is preserved by not sending information to any central server.
Learning Objectives:
Gain an overview of UI design frameworks. Understand expert systems concepts. Apply rule-based programming to symptom tracking. Create and evaluate an app to track symptoms.
Examination:
Final project presentation, demonstrator and final report.
- Literature
- Up-to-date literature recommendations are provided during the lectures.
- Zusätzliche Informationen
- Schlagwörter: ACR
Erwartete Teilnehmerzahl: 20, Maximale Teilnehmerzahl: 20
www: https://www.cdh.med.fau.de/2020/07/21/seminar-covid-19-digital-symptom-tracker-2/
Für diese Lehrveranstaltung ist eine Anmeldung erforderlich.
Die Anmeldung erfolgt von Dienstag, 15.9.2020, 08:00 Uhr bis Donnerstag, 12.11.2020, 18:00 Uhr über: StudOn.
- Verwendung in folgenden UnivIS-Modulen
- Startsemester WS 2020/2021:
- Advanced Context Recognition (ACR)
- Institution: Lehrstuhl für Digital Health
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