inf527 - Big Data Analytics and Clinical Decision Support (Complete module description)

Module label	Big Data Analytics and Clinical Decision Support
Module code	inf527
Credit points	6.0 KP
Workload	180 h
Institute directory	Department of Computing Science
Applicability of the module	Master's Programme Computing Science (Master) > Angewandte Informatik
Responsible persons	Wulff, Antje (module responsibility) Lehrenden, Die im Modul (authorised to take exams)
Prerequisites	No specific prior knowledge is required
Skills to be acquired in this module	In-depth understanding about the processing of medical data sets in the context of Data Analytics, Big Data in medicine and the development and importance of (decision) support tools. Professional competences The students know the sources, characteristics and diversity of medical data and the significance of Big Data in medicine know methods for designing (decision) support applications, including the processing of relevant data and information, with regard to a medical issue know application classes and types of (decision) support applications and tools know professional, organizational and regulatory requirements and framework conditions for data analysis and application development in the healthcare sector Methological competences The students can familiarize themselves with a medical, data-driven problem and solve it using familiar methods from the various areas of requirements elicitation, knowledge management, conception, implementation and evaluation can process given data in a targeted manner with regard to a medical- informational problem and discuss results critically can present and hand over results, e.g. decision support applications (especially for medical experts), in a way that adds value. Social competences The students learn the interdisciplinary exchange in this context and understand its importance and necessity develop, present and discuss the solutions from the exercises with others Self competences The students can assess their role and importance in the analysis of medical data and the development of medical support applications/tools recognize the limits of their (specialist) perspective and the added value of the domain knowledge of others critically reflect on problems and solutions, incorporating the methods they have learned
Module contents	Against the background of "Big Data in Medicine" and the increasing digitalization of medicine in general, the assigned lectures aim to impart knowledge on the characteristics, analysis and handling of medical data for the development, application and evaluation of (decision) support applications/tools in healthcare.
Recommended reading	Papademetris X, Quraishi AN, Licholai GP. Introduction to Medical Software. Foundations for Digital Health, Devices and Diagnostics, 978-1316514993 Further will be announced in the course
Links
Languages of instruction	German, English
Duration (semesters)	1 Semester
Module frequency	every winter term
Module capacity	unlimited
Teaching/Learning method	V+Ü

Type of course	SWS	Frequency	Workload of compulsory attendance
Lecture	2	WiSe	28
Exercise or project	2	WiSe	28
Total module attendance time			56 h

Examination	Prüfungszeiten	Type of examination
Final exam of module	at the end of the lecture period	Portfolio, written exam, practical exercise or oral exam The chosen form of examination will be announced in the first week of the course