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Master Medical Informatics

Fast facts

  • Department

    Informatik

  • Stand/version

    2026

  • Standard period of study (semester)

    4

  • ECTS

    0

Study plan

1. Semester3. Semester4. Semester

Scientific & Transversal Skills 1

  • PF
  • 4SWS
  • 6ECTS

MI Teamproject

  • PF
  • 2SWS
  • 6ECTS

Masters Thesis and Colloquium

  • PF
  • 0SWS
  • 30ECTS

Scientific & Transversal Skills 2

  • PF
  • 4SWS
  • 6ECTS

Research Project

  • PF
  • 4SWS
  • 12ECTS

Compulsory elective modules (20)

    • Compulsory elective modules 3. Semester

    • Compulsory elective modules 4. Semester

Module overview

1. Semester of study

Scientific & Transversal Skills 1
  • PF
  • 4 SWS
  • 6 ECTS

  • Number

    41021

  • Language(s)

    en

  • Duration (semester)

    1

  • Contact time

    60 h

  • Self-study

    120 h

Learning outcomes/competences

Knowledge:
After completing this module, students will be able to:
- know research methods and instruments in medical informatics (scientific field)
- Know and understand the culture of the different partner countries
- Know programming languages and modeling techniques
- Know web development techniques, languages, tools and frameworks
- Have IT skills in tools such as MS Excel, Word and PowerPoint
- German vocabulary and grammar skills at least at A1 level
- Knowledge of English (vocabulary and grammar) at least at C1 level
- Knowledge of the basic terminology and standards of medical informatics

Application and generation of knowledge: Students will be able to:
- apply research methods and tools in the scientific field
- work in an international and intercultural environment
- can program software in Java (alternatively: C# or Python)
- can model systems in UML
- can develop basic applications
- be able to use tools such as MS Excel, Word and PowerPoint with confidence
- speak, understand, read and write German to at least A1 level
- speak, understand, read and write English to at least C1 level

Communication and cooperation:
- Students can work together with international students in a cross-border project
- Students can adapt to and deal with different European cultures
- Students learn to communicate with people from different countries

Scientific self-image / professionalism:
- Students can plan and conduct scientific research in their subject area
- Students are aware of their own cultural background and can interact appropriately with other cultural backgrounds

Contents

The module offers students a series of smaller training units from which they can choose to fill gaps from previous studies or add specific skills. Up to 10 courses are offered in the winter semester (depending on availability). Intercultural training (see list below, no. 1) is mandatory for all students and is offered every semester. Students must choose 3 out of 6 optional training units (from No. 2-7+10). For students who do not have at least German A1, the German course (no. 8) is compulsory. For native German speakers, another language course must be completed at least at A1 level (no. 9). In order to close gaps in medical informatics, a refresher course is offered (no. 10). Further courses can be added depending on the needs analysis: Course Structure A selection of 8 compact courses will be offered in the initial phase of the Masters. More can be added depending on the analysis of students' needs:

1.
Intercultural Training (ICT): Intercultural training is designed to help students interact and work successfully with their teachers and fellow students at the university. It is also held as a team-building event for the new intake in the first semester. It is also intended to motivate students for later mobility/exchange with the partner universities.
2.
Compact course in web development (online): This course teaches the basics of web programming languages and frameworks. It is intended as a catch-up course for students with very little knowledge of web development.
3.
Compact programming course (Java, alternatively: C# or Python): This course teaches object-oriented programming skills in Java (decision is made before the start of the semester, change to C# or Python possible depending on which language is chosen in semester 1). It is intended as a catch-up course for students with little programming knowledge.
4th
Modeling of Software Systems (UML): This course teaches object-oriented modeling skills in UML. It is intended for students with limited software and systems engineering knowledge to catch up.
5.
Research Methods and Tools - Part A (RMT-A): Introduction to scientific methods and tools in the field of digital transformation. In addition, analysis of relevant scientific trends and communities. Students can prepare for academic work by following the sequence of RMT-A and RMT-B as well as a research seminar.
6.
Cross-border project A: During the November Master's block week or a workshop at a partner university, projects are formed with teams of students from several partners. They carry out projects, e.g. on industry cases, and present the results, e.g. in a pitching session.
7.
ICDL-Excel: Students who lack relevant IT skills can take part in the preparatory courses for the International Computer Driving License (ICDL) at Fachhochschule Dortmund and take the corresponding exams. The Excel course focuses on the use of Excel for data analysis and business intelligence.
8.
International Project Communication 1 e (German A1): Proof of German language proficiency at least at A1 level must be provided at the end of the semester. Appropriate courses will be organized and embedded in the weekly schedule.
9.
International Project Communication 1 g (other language): For students with German as their mother tongue (e.g. German/Austrian/Swiss nationals or students with a German-language previous degree (e.g. Bildungsinländer), a language certificate in another language (e.g. French, Spanish, Chinese, etc.) at least at A1 level is required. In the case of an English language certificate, level C2 is required.
10.
Introduction to the terminology and standards of medical informatics.

Teaching methods

  1. Intercultural Training (ICT): lectures and role plays
  2. Compact Web Development Course: online, set of LinkedIn courses with tests
  3. Compact Programming Course: online courses, programming tasks with reviews
  4. Modeling of Software Systems (UML): lectures, exercises and written exam  
  5. Research Methods and Tools - part A (RMT-A): lecture  
  6. Cross-Border Project A: project and presentation
  7. ICDL Excel: methods & tool training
  8. International Project Communication 1 e (German A1): language training
  9. International Project Communication 1 g (other language A1 or English C2): language training
  10. Medical informatics refresher

Participation requirements

None

Forms of examination

  1. Intercultural Training (ICT): exam
  2. Compact Web Development Course: online tests (LinkedIn)
  3. Compact Programming Course: review of the programming tasks, related questions
  4. Modeling of Software Systems (UML): written exam  
  5. Research Methods and Tools - part A (RMT-A): homework (paper assignment)
  6. Cross-Border Project A: presentation and discussion
  7. ICDL Excel: test
  8. International Project Communication 1 e (German A1): language test
  9. International Project Communication 1 g (other language A1 or English C2): language test
  10. Medical informatics refresher: exam

Requirements for the awarding of credit points

Successful completion of course Nr. 1, and 3 out of 7 technical courses (Nr. 2-7+10, graded), and language certificate (8 or 9). The weight for the mark of the graded courses is 1/3.

Applicability of the module (in other degree programs)

Depending on choice of courses

Importance of the grade for the final grade

5,00%

Literature

Loy, M., Niemeyer, P., Leuck, D. (2023). Learning Java: An Introduction to Real-World Programming with Java, 6th Edition, O-Reilly Media.

Miles, R., Hamilton, K. (2006). Learning UML 2.0: A Pragmatic Introduction to UML 1st Edition, O-Reilly Media.

Dresch, A., Pacheco Lacerda, D., & Valle Antunes Jr., J. A. (2015). Design Science Research: A Method for Science and Technology Advancement. Springer International Publishing Switzerland.

Bailey, S. (2018). Academic Writing – A Handbook for International Students (5th ed.). Routledge, New York.

Saunders, M., Lewis, P., Thornhill, A. (2019). Research Methods for Business Students, 8th edition, Pearson.

Bryman, A., Bell, E. (2011). Business research methods, 3rd Edition, Oxford University Press.

Creswell, J.Q. (2022). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 6th edition, Sage Publications.

Mayring, P. (2021). Qualitative content analysis, Sage Publications, 1st Edition.

Jordan, C. (2022). ICDL Excel: A step-by-step guide to spreadsheets using Microsoft Excel.

Scientific & Transversal Skills 2
  • PF
  • 4 SWS
  • 6 ECTS

  • Number

    41022

  • Language(s)

    en

  • Duration (semester)

    1

  • Contact time

    60 h

  • Self-study

    120 h

Learning outcomes/competences

Knowledge:
After completing this module, students will be able to:
- know advanced research methods and tools in medical informatics (scientific field)
- Know and understand business models in medical informatics
- Know TOGAF and enterprise IT & business architectures
- Know training concepts
- Have advanced IT skills in tools such as MS Excel
- know German vocabulary and grammar at least at A2 level
- know English vocabulary and grammar at least at C2 level

Application and generation of knowledge: Students will be able to
- apply research methods and tools in the scientific field
- be able to develop company-wide IT architectures based on case studies
- can train users in IT tools
- can use tools such as MS Excel at an advanced level
- speak, understand, read and write German at least at A2 level
- speak, understand, read and write English to at least C2 level

Communication and cooperation:
- Students can work together in medical informatics projects
- Students can train users in digital technologies
- Students learn to communicate with people at different levels of IT competence
- Students learn to communicate in different languages, especially German

Scientific self-image / professionalism:
- Students are able to plan and conduct scientific research in the field of medical informatics
- Students are aware of their own discipline and can interact appropriately with other disciplines
- Students can deal with contexts outside the IT technology field

Contents

The module is an extension of the module "Scientific and Transversal Skills 1" and offers students an additional series of smaller training units in which they can develop specific skills. Up to 8 courses are offered in the summer semester (subject to availability). Students must choose 3 out of 6 optional training units (from nos. 1-6). For students who do not have at least German A2, the German course (no. 7) is compulsory. For native German speakers, another language course must be completed at least at A1 level (no. 8). Further courses can be added according to the needs analysis.

Course structure
A selection of 8 compact courses is offered in the basic Master's program. More can be added depending on the analysis of students' needs:

1st compact course on advanced medical informatics knowledge
2. compact course on TOGAF: This course conducts a 1-week intensive workshop on the TOGAF framework (The Open Group Architecture Framework). The focus is on developing an enterprise architecture that combines the business and IT views
. 3. train-the-trainer on IT tools for projects: The aim of the course is to have IT students develop a training course, starting with the training concept (didactics, learning objectives), through the development of training materials, to the delivery of the training to students of a project management master.
4. research methods and tools - part B (RMT-B): Training on advanced scientific methods and tools in the field of digital transformation. The aim of the course is to prepare a concrete research project or a scientific publication. Students can continue the sequence of RMT-A and RMT-B plus a research seminar.
5. cross-border project B: During the May Master's block week or a workshop at a partner university, projects are formed with teams of students from several partner universities. They carry out projects, e.g. on clinical cases, and present the results, e.g. in a pitching session.
6 ICDL-Advanced Excel: This course prepares students for the Advanced Excel certificate of the International Computer Driving License (ICDL) and the corresponding exams. The course focuses on the use of Excel for data analysis and business intelligence.
7. international project communication 2 e (German A2): Proof of language proficiency in German at least at A1 level must be provided at the end of the semester. Appropriate courses are organized and embedded in the weekly schedule.
8. international project communication 2 g (other language): For students with German as their mother tongue (e.g. German/Austrian/Swiss citizens or students with German-language previous studies (e.g. Bildungsinländer), a language certificate in another language (e.g. French, Spanish, Chinese, etc.) at least at A1 level is required. In the case of an English language certificate, level C2 is required.

Teaching methods

1st Compact Course on Advanced medical informatics
2. compact course on TOGAF: online preparation, 1-week workshop based on case study
3. train-the-trainer on IT tools for projects: development of a training course (group work)
4. research methods and tools - part B (RMT-B): lecture and homework (paper writing)
5. Cross-Border Project B: project and presentation
6. ICDL Advanced Excel: methods & tool training
7. international project communication 2 e (German A2): language training
8. international Project Communication 2 g (other language A1 or English C2): language training

Participation requirements

None

Forms of examination

1. compact course on advanced medical informatics: exam
2. compact course on TOGAF: result presentation and review
3. train-the-trainer on IT tools for projects: evaluation of the training by participants
4. research methods and tools - part B (RMT-B): homework (paper assignment)
5. Cross-Border Project B: presentation and discussion
6. ICDL Advanced Excel: test
7. international project communication 2 e (German A2): language test
8. International Project Communication 2 g (other language A1 or English C2): language test

Requirements for the awarding of credit points

Successful completion of course no. 1 (graded), and 3 out of 5 technical courses (no. 2-6, graded), and language certificate (7 or 8). The weight for the mark of the graded courses is 1/4.

Applicability of the module (in other degree programs)

Depending on choice of courses

Importance of the grade for the final grade

5,00%

Literature

See “Scientific & Transversal Skills 1” for 4-8
(1)For Advanced medical informatics training material is provided for registered students
(2) specific training material is provided for registered students For TOGAF
(3) online courses of instructional design are provided for registered students

3D Computer Vision and Augmented Reality in Medicine
  • WP
  • 4 SWS
  • 6 ECTS

  • Number

    47614

  • Language(s)

    en

  • Duration (semester)

    1

  • Contact time

    60 h

  • Self-study

    120 h

Learning outcomes/competences

After completing the module, students will have acquired the following skills in the field of 3D computer vision and augmented reality for medical issues:

Knowledge (knowledge):
- know the importance of the fields of 3D computer vision and augmented reality for medicine and can explain them and research them for new areas of application.
- know mathematical methods, algorithms and data structures of camera calibration, tracking of medical devices and 3D reconstruction from projections and can explain them.
- know the interoperability standards for clinical images and the helpful ontologies for tasks in the healthcare sector (e.g. FHIR, DICOM, FMA)
- know the basics and limitations of machine learning, including deep learning methods for classification/segmentation/registration issues in 2D and 3D medical images
- know the mixed reality continuum and can classify medical applications within it.
- know modern methods of human-machine interaction that are helpful in the context of medical augmented reality issues.

Skills (abilities):
- can classify tasks for systems for 3D reconstruction from projections and solve them independently using methods of their own choice.
- can independently develop 3D computer vision and augmented reality solutions for medical issues, either alone or in a team, using suitable programming interfaces.
- can select suitable features for classification/segmentation/registration and use them for a machine learning model.
- can develop experiments to objectively assess the performance of 3D computer vision and augmented reality solutions.


Competencies (personal and social):
- can argue in a goal-oriented manner in presentations and discussions and deal with criticism objectively.
- can recognize and reduce existing misunderstandings between conversation partners.
- can discuss topics related to 3D computer vision and mixed reality with clinical specialists and propose solutions.
- can present scientific results in a way that is understandable to a specialist audience.




 

Contents

-    Introduction and motivation: 3D computer vision (including reconstruction from projections) and augmented reality applications in medicine
-    Overview of current standard software for 3D computer vision applications and introduction to selected programming interfaces, such as OpenCV, MevisLab libraries
-    Acquisition and analysis of depth images: active and passive methods
-    3D interaction methods
-    3D geometry, linear and affine mappings, quaternions
-    3D segmentation and registration
-    Camera calibration: spatial and projective geometry, camera models
-    3D reconstruction: stereo image analysis, epipolar geometry, correspondence analysis
-    Features and feature extraction: Edges and Gradients, Structure Tensor, Harris Corner Detector, Fourier Descriptors, SIFT
-    3D classification, deep learning

Teaching methods

  • Lecture in seminar style, with blackboard writing and projection
  • Solving practical exercises in individual or team work
  • Processing programming tasks on the computer in individual or team work

Participation requirements

None

Forms of examination

  • oral examination
  • examinations during the semester

Requirements for the awarding of credit points

passed oral examination (80% of the grade + 20% passed examination during the semester)

Applicability of the module (in other degree programs)

Master Medical Informatics, Master Medical Informatics

Literature

•    Uhl, J.-F. und andere; Digital Anatomy, Springer, 2021 (elektronisch in der Bibliothek vorhanden)
•    Schmalstieg, D. und Höllerer, T.: Augmented Reality: Principles and Practice (Usability), Addison Wesley, 2016. (elektronisch in der Bibliothek vorhanden)
•    Aukstakalnis, S. Practical Augmented Reality, Addison Wesley, 2016.
•    Szeliski, R.; Computer Vision: Algorithms and Applications, Springer, 2010
•    Hartley, R. et al.; Multiple View Geometry in Computer Vision; Cambridge University Press; 2. Auflage; 2004
•    Toennis, K. D.; Guide toMedical imageAnalysis; 2te Auflage, Springer, 2017
•    Preim, B. und Botha, C.; Visual Computing for Medicine , 2nd edition, Morgan Kaufman Publishers, 2014. (elektronisch in der Bibliothek vorhanden)
•    Forsyth, D. A. and Ponce, J.; Computer Vision - a modern approach, Prentince Hall, 2003
•    Tönnis, M.; Augmented Reality: Einblicke in die Erweiterte Realität; Springer; 2010
•    Furht, B. et al.; Handbook of Augmented Reality; Springer; 2011

 

Advanced Methods in Biomedical Signal and Image Processing
  • WP
  • 4 SWS
  • 6 ECTS

  • Number

    47612

  • Language(s)

    en, de

  • Duration (semester)

    1

  • Contact time

    60 h

  • Self-study

    120 h

Learning outcomes/competences

Knowledge and understanding:

After successfully completing the module, students will be able to:

  • exemplify, select and apply the essential steps of bio-signal analysis using the example of the detection of QRS complexes in ECG signals 
  • explain and calculate advanced mathematical models and methods of signal and image transformation and describe their formal principles
  • describe and differentiate between machine learning methods, in particular deep learning, for analyzing biosignals and medical images

 

Use, application and generation of knowledge:

After successfully completing the module, students will be able to:

  • view scientific literature using the example of QRS detection in ECG signals and independently understand how the procedures work
  • analyze biosignals and medical images independently and transfer the methods covered to new tasks
  • implement and test signal and image transformation methods using the Python, Matplotlib, NumPy and SciPy software ecosystems and apply them to practical problems
  • evaluate the accuracy of the methods using large data sets of ECG signals from a public database (PhysioNet) and assess the results
  • implement machine learning and deep learning methods with SciPy and PyTorch and optimize the training

 

Communication and cooperation:
After successfully completing the module, students will be able to:

  • work on and solve programming tasks in small teams on the computer
  • experiment in small groups in a targeted manner, for example to optimize a deep learning model
  • to critically reflect on and, if necessary, defend their own solution approaches and criticize other solution approaches


    • Scientific self-image / professionalism:
    After successfully completing the module, students will be able to:

    • take an evidence- and research-based approach to creating solutions
    • solve technical issues typical of the profession, such as analyzing biosignals and medical images
    • to abstract from concrete problems based on examples related to the occupational field and to recognize basic solution approaches
    • justify and justify the use of their chosen methods to professional representatives
    • to assess the importance of interdisciplinary collaboration with experts from medicine, computer science and other disciplines

    Contents

    • Biosignal processing: signal analysis in electrocardiography (ECG), electroencephalography (EEG) and electrooculography (EOG)
    • Important signal and image transforms and their applications in medical technology:  Fourier Transform, Short-time Fourier Transform, Wavelet Transform, Hilbert Transform, Discrete Cosine Transform
    • Sampling theorem, signal filtering, polynomial curve fitting, mathematical morphology
    • Multi-scale parameter estimation
    • Image compression methods: Huffman-, Arithmetic-, LZW-, Bit-plane-Coding; JPEG compression

     

    Teaching methods

    • Lecture in seminar style, in interaction with the students, with blackboard writing and projection
    • Lecture-accompanying and closely interlinked exercise in interaction with the students, with blackboard writing and projection. Explanation of the task and joint development and outlining of a solution
    • Internship accompanying the lecture and closely interlinked in terms of content; working on programming tasks on the computer in individual or team work


     

    Participation requirements

    None

    Forms of examination

    The module examination consists of a written exam in which students should recall and apply basic knowledge of advanced methods of signal and image processing. In addition, they should be able to transfer this knowledge to practical problems and apply it if necessary. This includes creating short scripts in Python or completing given scripts.
    Duration: 90 minutes

    Requirements for the awarding of credit points

    The written examination is graded and must be completed with a minimum grade of sufficient (4.0)

    Applicability of the module (in other degree programs)

    Master's degree in Medical Informatics

    Literature

    • Selesnick, I. et al.; Signal Processing in Medicine and Biology - Emerging Trends in Research and Applications; Springer; 2020
    • Handels, H.; Medizinische Bildverarbeitung; Vieweg+Teubner; 2. Auflage; 2009
    • Birkfellner W.; Applied Medical Image Processing; Taylor & Francis; 2010
    • Nischwitz, A. et al.; Computergrafik und Bildverarbeitung: Band II: Bildverarbeitung; Vieweg+Teubner; 4. Auflage, 2020
    • Bankman, I. et al.; Handbook of Medical Image Processing and Analysis; Academic Press; 2. Auflage; 2009
    • Lyons, R.; Understanding Digital Signal Processing; Prentice Hall; 3. Auflage; 2010
    • Sonka, M. et al.; Image Processing, Analysis, and Machine Vision; Thomson; 3. Auflage; 2008

    Advanced Regression Methods
    • WP
    • 4 SWS
    • 6 ECTS

    • Number

      46801

    • Language(s)

      en, de_en

    • Duration (semester)

      1

    • Contact time

      60 h

    • Self-study

      120 h

    Learning outcomes/competences

    After successfully completing the module, students will be able to extract information from data using regression methods. They will be able to independently recognize which type of statistical model from the field of regression analysis is suitable for a problem and name the model assumptions. They can use suitable software to adapt a model to data sets and explain why which method is used for parameter estimation and model selection. Students can set up a statistical experimental plan for a screening or optimization question, collect and analyze data themselves and prepare documentation in report form.

     

    Contents

    The module teaches assumptions and definitions of various statistical models from the field of regression models. In addition to classical linear models, analysis of variance, generalized linear models and Gaussian process models are covered. The entire process of statistical data analysis is covered, starting with model assumptions, hypothesis testing, parameter estimation using maximum likelihood and the least squares method, residual analysis, variable selection, through to model interpretation and forecasting and forecasting intervals. The basics of statistical experimental design (experimental design, experimental range, coding, randomization, repetitions, block formation) and various experimental designs (screening and optimization plans, space-filling plans) are taught for the collection of your own data set.
    models, )

    Teaching methods

    Lecture in seminar style, with blackboard writing and projection, solution of practical exercises in individual or team work, project work accompanying the lecture on practical applications with documentation and presentations
     

    Participation requirements

    None

    Forms of examination

    Project work with oral examination

    Requirements for the awarding of credit points

    Successful project work

    Applicability of the module (in other degree programs)

    • Master of Computer Science
    • Master's degree in Medical Informatics
    • Master's degree in Business Informatics

    Literature

    • Fahrmeir, L., Künstler, R., Pigeot, I., Tutz, G. (2016), Statistik - der Weg zur Datenanalyse, 8. Aufl., Springer, Berlin.
    • Fahrmeir, L., Kneib, Th., Lang, S., Marx, B. (2013), Regression: Models, Methods and Applications, Springer, Berlin.
    • Dobson, A.J., Barnett, A.G. (2018), An Introduction to Generalized Linear Models, 4th edition, Taylor & Francis Ltd, Boca Raton.
    • Sievertz, K., van Bebber, D., Hochkirchen, Th. (2017) Statistische Versuchsplanung - Design of Experiments (DoE), 4te Auflage, Springer Vieweg, Berlin.

    Advanced Telemedicine Applications and Technologies
    • WP
    • 4 SWS
    • 6 ECTS

    • Number

      47401

    • Language(s)

      en

    • Duration (semester)

      1

    • Contact time

      60 h

    • Self-study

      120 h

    Learning outcomes/competences

    Knowledge and understanding

    The students:

    • have an in-depth understanding of telemedicine projects and can analyze, classify and describe them using a systematic methodology.

    • know the legal and regulatory framework in the area of data protection, especially with regard to telemedicine applications.

    • understand the concepts and requirements of the electronic case record (eFA) and the electronic patient record (eEPA) and can assess their suitability for various application scenarios.

    • can differentiate between telemedicine, teletherapy and telemonitoring and classify new issues accordingly in existing concepts.

    • know the basics of digital health applications (DiGA) and their significance for medical care.

    • understand the requirements of the telematics infrastructure (TI) and its impact on the implementation and use of telemedicine applications.

    • have knowledge of medical vital signs sensors and their possible applications in telemedicine.

    • know the regulatory requirements for medical devices and their approval in a telemedical context.

    Use, application and generation of knowledge

    Students are able to:

    • to analyse and evaluate telemedicine projects, taking into account technical, organizational and legal aspects.

    • to systematically consider data protection requirements when planning and implementing telemedicine solutions.

    • Categorize communication solutions such as eFA and eEPA in specific application contexts and make informed decisions on their suitability for different scenarios.

    • identify new developments in the field of telemedicine, teletherapy and telemonitoring and derive their potential benefits for different use cases.

    • understand the financing options for telemedicine applications and assess their economic viability.

    • apply evaluation methods to scientifically test the effectiveness and evidence of telemedicine solutions.

    • to use medical vital signs sensors for monitoring and diagnostics in telemedicine applications.

    • to classify medical devices in telemedicine scenarios with regard to regulatory requirements and technical specifications.

    Communication and cooperation

    The students:

    • work effectively in interdisciplinary teams and organize themselves within project groups.

    • conduct qualified interviews with experts and users to identify requirements and challenges of telemedicine solutions.

    • present their analyses and solutions professionally and in a target group-oriented manner.

    • reflect on and discuss telemedical developments and their effects in interdisciplinary specialist discussions.

    Scientific self-image / professionalism

    The students:

    • have an in-depth understanding of market-relevant telemedicine applications and can evaluate them in the context of healthcare.

    • recognize technological trends and regulatory developments in the field of telemedicine and can critically question them.

    • reflect on the social, ethical and economic implications of telemedicine solutions.

    • are able to continuously familiarize themselves with new telemedicine topics and develop their expertise using scientific methods.

    • understand the importance of evidence-based telemedicine and can apply evaluation criteria for digital health applications and telemedicine concepts.

    • are familiar with the regulatory requirements for medical devices and can take these into account in the design and development of telemedicine applications.

    Contents

    • Definitions of telecooperation, telemonitoring and teletherapy with examples
    • Introduction of a system for telemedical projects (from the care problem, solution approach and cooperation structures to cost-benefit analysis and business model)
    • Data protection in the field of telemedicine and telematics
    • Communication solutions in the healthcare sector and their relation to telemedicine
    • DiGA (digital health applications)
    • Health telematics
    • Sensor technology and vital signs
    • Regulations for medical devices
    • Financing in the German healthcare system
    • Evidence of telemedical solutions
    • Application of this content to your own case study, which will be developed during the semester and presented at the end

    Teaching methods

    • Lecture in seminar style, with blackboard and projection
    • seminar-style teaching with flipchart, smartboard or projection
    • project work accompanying the lecture with final presentation

    Participation requirements

    None

    Forms of examination

    • Project work with final presentation of the results (30 minutes)

    Requirements for the awarding of credit points

    • passed project work with final presentation (30 minutes)

    Applicability of the module (in other degree programs)

    Master's degree in Medical Informatics

    Literature

    • Tagungsbände Telemed
    • Tagungsunterlagen DGTelemed
    • Bartmann et al; Telemedizinische Methoden in der Patientenversorgung; Deutscher Ärzteverlag 2012

    Advanced Web Engineering
    • WP
    • 4 SWS
    • 6 ECTS

    • Number

      46854

    • Language(s)

      en

    • Duration (semester)

      1

    • Contact time

      60 h

    • Self-study

      120 h

    Learning outcomes/competences

    Knowledge and understanding: After completing this module, students will have deepened and intensified their skills,
    • analyze and differentiate between different web architectures and central architectural patterns of web applications,
      • to name and categorize important web standards and technologies.
    Use, application and generation of knowledge: After completing this module, students will be able to
    • implement a complex web engineering task as part of a project lasting several weeks,
      • derive and design a suitable web architecture to solve a specific problem,determine and combine suitable web standards and technologies to implement this architecture,use advanced web engineering tools such as development environments, bundlers, scaffolding and transpilers
    Communication and cooperation: After completing this module, students will be able to,
    • develop and implement solutions cooperatively in a team,
      • present, explain and discuss their ideas and solutions in various formats such as group presentations, code reviews, lightning talks or pitches, especially in front of a specialist audience (e.g. guests/partners from industry or research projects).
    Scientific self-conception/professionalism: After completing this module, students will be able to
    • select and apply industrial and scientific best practices from the field of web engineering as appropriate to the situation,
      • reflect on and evaluate feedback, e.g. from code reviews with experts, and use the feedback received independently to improve their solutions.

    Contents

    Module description:
    In this module, students gain an overview of the architectures of complex web applications and analyze their differences and areas of application. They learn how corresponding web applications can be implemented by selecting and using suitable client- and server-side technologies.

    Module structure:
    The module covers the following topics:

    1. Brief review of the basics of creating web pages with HTML, CSS and JavaScript (Bachelor material)
    2. Identification, analysis and differentiation of architectures of modern web applications:
      1. Architectural patterns such as MVC and its variants (MVVM, MVP, etc.)
      2. Request-based and component-based web frameworks
      3. Single vs. multi-page applications, server-side rendering, client-side rendering, hybrid approaches (e.g. rehydration, resumability)
      4. Reactive programming/streaming
    3. In-depth study of server-side technologies for the development of web applications (e.g. with Java, JavaScript)
    4. In-depth study of client-side concepts and technologies for the development of web applications (e.g. component-oriented development, state management, routing)
    5. Overview of current developments in web standards and research (e.g. web components, WebAssembly)

    Teaching methods

    • Flipped/Inverted Classroom:
      • Online e-learning materials with interactive slides and videos (asynchronous self-study)
      • Interactive face-to-face events for tasks and exercises based on practical and research examples (e.g. coding, group exercises, lightning talks), for additional in-depth study and for answering and discussing questions
    • Project-oriented internship: project task that is worked on in teams over the entire semester
    • Guest lectures with experts and current topics from research and industry

    Participation requirements

    None

    Forms of examination

    Written examination paper (scope: 60%, duration: 60-90 minutes) or oral examination (scope 60%, duration: 20-25 minutes); examinations during the semester (project-related work, scope: 40%)

    Requirements for the awarding of credit points

    Passed written examination or passed oral examination (according to the current examination schedule); passed examinations during the semester

    Applicability of the module (in other degree programs)

    • Master's degree in Business Informatics
    • Master of Computer Science
    • Master's degree in Medical Informatics
    • Master's degree in Digital Transformation

    Literature

    • Simpson, Kyle (2015-2020): You Don’t Know JS (Yet), Volume 1-6, O’Reilly/Independently published
    • Ullenboom, Christian (2024): Spring Boot and Spring Framework 6, Rheinwerk Computing
    • Jacobson, Daniel; Brail, Greg; Woods, Dan (2011): APIs: A Strategy Guide: Creating Channels with Application Programming Interfaces, O'Reilly
    • Masse, Mark (2011): REST API Design Rulebook: Designing Consistent Restful Web Service Interfaces, O’Reilly
    • Porcello, Eve; Banks, Alex (2018): Learning GraphQL: Declarative Data Fetching for Modern Web Apps, O’Reilly
    • Bass, Len; Clements, Paul; Kazman, Rick (2021): Software Architecture in Practice, SEI Series in Software Engineering, Fourth Edition, Addison-Wesley Professional
    • Osmani, Addy (2023): Learning JavaScript Design Patterns: A JavaScript and React Developer's Guide, Second Edition, O’Reilly

    Relevante Standards:

    Anerkannte Wahlpflichtprüfungsleistung 1
    • WP
    • 0 SWS
    • 6 ECTS

    • Number

      46995

    • Language(s)

      en

    • Duration (semester)

      1

    Anerkannte Wahlpflichtprüfungsleistung 2
    • WP
    • 0 SWS
    • 6 ECTS

    • Number

      46996

    • Language(s)

      en

    • Duration (semester)

      1

    Anerkannte Wahlpflichtprüfungsleistung 3
    • WP
    • 0 SWS
    • 6 ECTS

    • Number

      46997

    • Language(s)

      en

    • Duration (semester)

      1

    Anerkannte Wahlpflichtprüfungsleistung 4
    • WP
    • 0 SWS
    • 6 ECTS

    • Number

      46998

    • Language(s)

      en

    • Duration (semester)

      1

    Design and Modeling of Complex Software Architectures
    • WP
    • 4 SWS
    • 6 ECTS

    • Number

      46862

    • Language(s)

      en

    • Duration (semester)

      1

    • Contact time

      60 h

    • Self-study

      120 h

    Learning outcomes/competences

    Knowledge and Understanding: Upon completion of this module, students will be able to

    • distinguish between the basic principles of software design,
      • distinguish and categorize relevant tools and methods for domain-oriented design,name and classify current research approaches to modeling software architectures


    Use, application and generation of knowledge: After completing this module, students will be able to

    • analyze a complex problem/domain and break it down into sub-problems/domains,
      • realize a complex software design task as part of a project lasting several weeks,
    • select appropriate software design principles and apply them to specific application scenarios,
      • distinguish, analyze and apply central patterns at the macro- and micro-architecture level,select, combine and apply suitable methods for domain-driven design


    Communication and cooperation: After completing this module, students will be able to

    • develop and implement solutions cooperatively in a team, 
    • select and apply suitable methods for the interdisciplinary development of solutions, especially together with domain experts without a technical background,
      • present, explain and discuss their ideas and solutions in various formats such as group presentations, code reviews, lightning talks or pitches, especially in front of a specialist audience (e.g. guests/partners from industry or research projects).


    Scientific self-conception/professionalism: After completing this module, students will be able to

    • select and apply best practices from industry and academia for software design,
      • reflect on and evaluate feedback, especially from non-technical domain experts, and independently implement the feedback received to improve their solution concepts

    Contents

    Module description:
    In this module, students deepen their skills in the design of software architectures for complex systems. Students learn how to design a scalable, robust and maintainable domain-driven software architecture by selecting and applying suitable principles, patterns and methods. The analysis and discussion of such software architectures is based on practical examples and concrete solutions from research projects.

    Module structure:
    The module covers the following topics:

    1. Short repetition of the Bachelor material on software design (e.g. design patterns according to Gamma et al., separation of concerns, layered architecture)
    2. In-depth aspects of software design:
      1. Principles (e.g. loose coupling - high cohesion, SOLID)
      2. Architectural patterns (e.g. ports and adapters, CQRS)
      3. Methods (e.g. domain-driven design, WAM approach)
    3. Characteristics and patterns of modern architectural styles (e.g. modular architectures, event-based architectures, microservice architectures)
    4. Model-driven design, development and reconstruction of software architectures

    Teaching methods

    • Flipped/Inverted Classroom:
      • Online e-learning materials with interactive slides and videos (asynchronous self-study)
      • Interactive face-to-face events for tasks and exercises based on practical and research examples (e.g. coding, group exercises, lightning talks), for additional in-depth study and for answering and discussing questions
    • Project-oriented internship: project task that is worked on in teams over the entire semester
    • Guest lectures with experts and current topics from research and industry

    Participation requirements

    None

    Forms of examination

    Written examination paper (scope: 60%, duration: 60-90 minutes) or oral examination (scope 60%, duration: 20-25 minutes); examinations during the semester (project-related work, scope: 40%)

    Requirements for the awarding of credit points

    Passed written examination or passed oral examination (according to the current examination schedule); passed examinations during the semester

    Applicability of the module (in other degree programs)

    • Master's degree in Business Informatics
    • Master of Computer Science
    • Master's degree in Medical Informatics
    • Master's degree in Digital Transformation

    Literature

    • Vernon, Vernon (2016): Domain-Driven Design Distilled, Addison-Wesley
    • Evans, Eric (2003): Domain-Driven Design: Tackling Complexity in the Heart of Software, Addison-Wesley
    • Richardson, Chris (2018): Microservice Patterns: With examples in Java, Manning
    • Martin, Robert C. (2017): Clean Architecture: A Craftsman's Guide to Software Structure and Design, Pearson
    • Lilienthal, Carola (2019): Sustainable Software Architecture: Analyze and Reduce Technical Debt; dpunkt.verlag
    • Bass, Len; Clements, Paul; Kazman, Rick (2021): Software Architecture in Practice, SEI Series in Software Engineering, Fourth Edition, Addison-Wesley Professional
    • Gamma, Erich; Helm, Richard; Johnson, Ralph; Vlissides, John (1994): Design Patterns: Elements of Reusable Object-Oriented Software, Addison-Wesley
    • Combemale, Benoit; France, Robert; Jézéquel, Jean-Marc; Rumpe, Bernhard;  Steel, James; Vojtisek, Didier (2016): Engineering Modeling Languages. CRC Press
    • Rademacher, Florian (2022). A language ecosystem for modeling microservice architecture, Phd Thesis, https://dx.doi.org/doi:10.17170/kobra-202209306919

    Distributed and Parallel Systems
    • WP
    • 4 SWS
    • 6 ECTS

    • Number

      10121

    • Language(s)

      en

    • Duration (semester)

      1

    • Contact time

      60 h

    • Self-study

      120 h

    Learning outcomes/competences

    Knowledge
    • Knows theory of distributed and parallel systems
    • Knows critical issues concerning reliable distributed systems
    • Knows recent research about partitioning and scheduling for cyber physical systems
    Skills
    • Can assess the feasibility of distributed CPS
    • Can implement algorithms for distributed embedded systems
    • Can model the behavior of distributed CPS
    • Can apply state of the art tools and can develop new tools for distribution
    Competence - attitude
    • Can setup tooling and design flows
    • Can discuss distribution issues with computer scientists
    • Understands the potential of concurrency in CPS

    Contents

    Distributed systems are groups of networked computers and/or embedded systems, which have a common goal for their work. The terms distributed computing and parallel computing have a lot of overlap and frequently the term concurrent computing is used in this field. There is no clear distinction between them. This course is a prerequisite for the deeper understanding of multicore and manycore systems. It builds the theoretical core knowledge about cyber physical systems (CPS) and about the current state of research in the field of embedded distributed systems.

    Course Structure
    1. architectures for distributed systems (in principle)
    2. communication
        a. Synchronous, Asynchronous
        b. Peer-to-Peer, Broadcast, Multicast
        c. Protocols
    3. time and states
        a. States and Timestamps
        b. Clocks
    4. coordination and agreement
        a. Transactions and Concurrency Control
        b. Deadlocks
        c. Replication and Fault Tolerance
    5. scheduling/partitioning/distribution (multicore/manycore)
    6. cyber physical systems (CPS)
    7. dependent systems
    8. programming paradigms and methods

    Skills trained in this course: theoretical and methodological skills

    Teaching methods

    • Lectures & Exercises, AMALTHEA and TA tool labs
    • e-learning modules on theoretical informatics, tool tutorials
    • Presentation and discussion of an industry case by a partner company (e.g. Bosch, BHTC, TA)

    Participation requirements

    None

    Forms of examination

    Written Exam (60 min) at the end of the course (50%) and individual homework (50%): paper/report about a recent topic from CPS research

    Requirements for the awarding of credit points

    Passed exam and passed semester assignments
     

    Applicability of the module (in other degree programs)

    Input for:
    • MOD2-01- Mechatronic Systems Engineering
    • MOD2-02 - Microelectronics & HW/SW Codesign
    • MOD-E03 - SW Architectures for Embedded and Mechatronic Systems

    Importance of the grade for the final grade

    5,00%

    Literature

    • G. Coulouris, J. Dollimore, T. Kindberg, G.Blair: Distributed Systems: Concepts and Design (5th ed.), Addison Wesley, May 2011
    • Hermann Kopetz, Real-Time Systems: Design Principles for Distributed Embedded Applications (Real-Time Systems Series), Springer, April 2011
    • P. Linington, Z. Milosevic, A. Tanaka, A. Vallecillo. Building Enterprise Systems with ODP: An Introduction to Open Distributed Processing, Chapman & Hall/CRC, September 2011
    • P. Koopmann. Better Embedded System Software, Drumnadrochit Education, 2010
    • Research Papers: Lamport, Chandy & Lamport
    • Other recent research papers

    Epidemiology and Applications in Healthcare
    • WP
    • 4 SWS
    • 6 ECTS

    • Number

      47402

    • Language(s)

      en

    • Duration (semester)

      1

    • Contact time

      60 h

    • Self-study

      120 h

    Learning outcomes/competences

    The course deals with the theoretical/scientific fundamentals of epidemiology and their practical application. After successfully completing the course, students will have acquired the following skills:

    Knowledge (knowledge):
    - know the most important basic technical terms from the fundamentals of epidemiology and health services research and can name them
    - know the difference between primary data collection and secondary data
    - know epidemiological measures such as morbidity measures, mortality measures, absolute risk, risk difference, relative risk, odds ratio and can name them
    - know the most important epidemiological study types and know the typical areas of application
    - know the most important sources of distortion, bias and confounding and can describe these formally and use them to assess the limits of epidemiological publications

    Skills
    - can independently calculate epidemiological measures
    - can determine suitable study types and select them for their own applications
    - can design questions for epidemiology and health services research studies
    - are able to select suitable methods to avoid bias
    - can scrutinize and discuss the implementation and presentation of results of epidemiological studies (in publications)
    - can plan, carry out, analyze and document epidemiology mini-projects independently in a team

    Competencies (personal and social skills)
    - can formulate ideas and proposed solutions orally and in writing
    - can solve tasks in exercises and projects independently and present the results
    - can independently develop and present theoretical content from scientific literature
    - can develop solutions cooperatively in the exercise and project phases
    - can cooperatively plan, distribute and jointly carry out tasks for solutions in the project phases
    - can argue in discussions in a goal-oriented manner and deal with criticism objectively
    - can present the results of group work together
    - can evaluate project results and formulate suggestions for improvement

    Contents

    - Study types (descriptive studies, analytical studies, randomized controlled trials, observational studies, cross-sectional or prevalence studies, cohort studies, case-control studies), areas of application, advantages and disadvantages of study types, sources of bias, bias and confounding     

    - Statistical methods of epidemiology    
      Methods and techniques of data presentation
      Descriptive statistics and basics of correlation analysis    

    - Areas of application of epidemiology    
      Cardiovascular epidemiology, cancer epidemiology, occupational epidemiology, health policy

    - Research and methodology:
      Formulating epidemiologically testable research questions and hypotheses
      Develop study designs according to the research question
      Methods and concepts of knowledge acquisition from routine health care data
      Data management and  Data analysis in the context of epidemiological research / health services research
     
    - Forms of presentation (poster, lecture, podcast, etc.)

    Teaching methods

    • Lecture in seminar style
    • Processing of practical projects in individual or team work

    Participation requirements

    None

    Forms of examination

    Presentation incl. presentation

    Requirements for the awarding of credit points

    Successful presentation

    Applicability of the module (in other degree programs)

    Master's degree in Medical Informatics

    Literature

    Kreienbrock, L, Schach S: Epidemiologische Methoden. Spektrum Akademischer Verlag GmbH, Heidelberg, Berlin

    Wird zur Lehrveranstaltung bekannt gegeben

    Formal Methods
    • WP
    • 4 SWS
    • 6 ECTS

    • Number

      46859

    • Language(s)

      en

    • Duration (semester)

      1

    • Contact time

      60 h

    • Self-study

      120 h

    Learning outcomes/competences

    Formal methods are languages for modeling software systems at a certain level of abstraction. As they have formal semantics, the models described in this way can be analyzed for correctness. This is particularly important for software-intensive systems.

    The lecture imparts knowledge and skills in the modeling and analysis of software systems. Students should also be able to select suitable languages and analysis techniques for modeling.

    Technical and methodological competence:

    • apply the theory of formal methods
    • Design, implement and analyze formal models of complex systems
    • evaluate different formal methods and models with regard to criteria

    Self-competence:
    The student can present ideas and proposed solutions in writing and orally, the independent presentation of solutions contributes to the development of self-confidence/professional competence; the development of strategies for acquiring knowledge and skills is supported by the combination of (seminar-style) lectures with independent development of the contents of scientific literature
    .
    Social skills:
    Cooperation and teamwork skills are trained during the exercise and project phases. The student can argue in a goal-oriented manner in discussions and deal with criticism objectively; he/she can recognize and reduce existing misunderstandings between discussion partners. Results from group work can be presented together.

    Contents

    • Embedding formal methods in the software development cycle, process models
    • Methods for formal program development on a large scale
    • Formalisms that are used in today's program development systems:
      • Algebraic specification techniques
      • State-oriented and time-dependent specifications
      • Treatment of concurrency
    • Procedures for the verification and validation of formal development steps, formal specification languages
    • Tools for formal program development

    Teaching methods

    • Lecture in interaction with the students, with blackboard writing and projection
    • Lecture in seminar style, with blackboard and projection
    • Solving practical exercises in individual or team work

    Participation requirements

    None

    Forms of examination

    • Written examination paper (scope 70%, duration 90min)
    • Examination performance: Project (scope 30%)

    Requirements for the awarding of credit points

    • passed written exam
    • passed project

    Applicability of the module (in other degree programs)

    Master's degree in Computer Science

    Literature

    • Reisig, W. (2013): Understanding Petri Nets – Modeling Techniques, Analysis Methods, Case Studies, Springer
    • Clarke, E.M., Grumberg, O., Peled, D.A. (1999): Model Checking, MIT Press
    • Baier, C., Katoen, J.-P. (2008): Principles of Model Checking, MIT Press
    • Spivey, J.M. (2001): The Z Reference Manual (https://github.com/Spivoxity/zrm/blob/master/zrm-pub.pdf)
    • Ruhela, V. (2012): Z Formal Specification Language – An Overview, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume 01, Issue 06
    • http://www.tapaal.net
    • http://www.uppaal.org

    Human Centered Digitalization
    • WP
    • 4 SWS
    • 6 ECTS

    • Number

      48202

    • Language(s)

      en

    • Duration (semester)

      1

    • Contact time

      60 h

    • Self-study

      120 h

    Learning outcomes/competences

    Learning outcomes

    Knowledge
    • Knows relevant theoretical foundations, area: computer science and society
    • Knows methodical background of case studies and surveys
    • Is aware of critical limitations of methods for evaluating impact
    Skills
    • Can analyze the impact of changes in information technology on individuals, environment and society, based upon a given past scenario
    • Can evaluate, analyze (and within limits predict) the impact of new products/services on individuals, environment and society, during the concept and development phase
    • Can conduct methodologically structured evaluations (e.g. field observation, lab tests) and surveys
    Competence - attitude
    • Can discuss impacts of changes in information technology on individuals, environment and society with experts
    • Can advise during product/service development potential impacts of product/service structure/features on individuals, environment and society
    • Understands scientific publication in the related areas

    Contents

    Course Description

    Digitalization in private and professional domains is influencing intensely and sometimes even revolutionizing people's life, the way they interact with systems, the way they interact between each other, the way a society changes. Within this course those influences will be addressed from two different viewpoints. From an analytical perspective, former and current developments and their influences will be analyzed and then projected on future trends. From a constructive perspective, those potential influences of e.g. a product or service currently in development will be taken into account to shape the prospective solution.


    Course Structure
    • Basic Overview "Computer Science & Society"
    • Ethics in computer science
    • Digital media and art
    • Surveillance and privacy
    • Artificial intelligence and responsibility
    • Case Studies "Disruptive Changes by Information Technology"
    • Digitalization of work life & work environments, processes, products and services
    • Evaluation of impacts (personal, environment, society)

    Application Focus

    Case Studies "Disruptive Changes by Information Technology"
    Involvement in projects: Analyzing impacts and potentials for news products and services


    Scientific Focus

    (Pre-)Studies & surveys about socioeconomic impacts of digitalization
    Paper with literature review/state-of-the-art


    Skills trained in this course: theoretical knowledge, practical skills and scientific competences

    Teaching methods

    Teaching and training methods
    • Theoretical knowledge: e-learning modules on formal methods, tool tutorials
    • Practical skills: Projects with MechatronicUML
    • Scientific Competences: literature review and synthesis into a paper

    Participation requirements

    None

    Forms of examination

    Assessment of the course: Practical Skills (50%): Group work and/or individual task, case studies and projects => demonstration/presentation of the result an Scientific Competences (50%): written paper (literature review, study report or survey, approx. 25 pages) and presentation (in class or at a student conference, e.g. International Research Conference Dortmund)

    Requirements for the awarding of credit points

    Passed exam and passed semester assignments

    Applicability of the module (in other degree programs)

    Input for:

    R&D project & Thesis

    Importance of the grade for the final grade

    5,00%

    Literature

    References

    Changing conference proceedings and journals, e.g.

    ICT and Society: 11th IFIP TC 9 International Conference on Human Choice and Computers, HCC11 2014, Turku, Finland, July 30 - August 1, 2014, Proceedings 431 IFIP Advances in Information and Communication Technology, Springer, 2014, ISBN 3662442086, 9783662442081

    eHealth: Legal, Ethical and Governance Challenges, Carlisle George, Diane Whitehouse, Penny Duquenoy, Springer Science & Business Media, 2012, ISBN 3642224741, 9783642224744

    An Ethical Global Information Society: Culture and democracy revisited
    IFIP Advances in Information and Communication Technology, Jacques J. Berleur, Diane Whitehouse, Springer, 2013, ISBN 0387353275, 9780387353272

    Human Choice and Computers: Issues of Choice and Quality of Life in the Information Society
    Band 98 von IFIP Advances in Information and Communication Technology, Klaus Brunnstein, Jacques Berleur, Springer, 2013, ISBN 0387356096, 9780387356099

    IT Nets
    • WP
    • 4 SWS
    • 6 ECTS

    • Number

      46833

    • Language(s)

      en

    • Duration (semester)

      1

    • Contact time

      60 h

    • Self-study

      120 h

    Learning outcomes/competences

    Technical and methodological competence:
    The student understands the principles, protocols and architecture of computer networks and the applications based on them. He/she applies network design procedures on layer 2 and layer 3, carries out the configuration of network components (router, switch) and plans the setup of virtual networks. He/she understands the design and implementation of communication protocols and is able to design and configure distributed systems with physical and virtual network components.

    Social skills:
    Based on practical demonstrations and experience gained through practical exercises, he/she is able to evaluate typical and recognized technologies and procedures in the areas of data network communication and the use of virtual network systems.

    Contents

    • Communication and reference models;
    • Theoretical methods for capacity planning and calculation based on statistical models and Markov chains;
    • Network algorithms for switching - Spanning Tree Protocol - and routing - Open Shortest Path First
    • Wide traffic solutions, such as Multi Protocol Label Switching;
    • Virtualized network devices using the example of CumulusVX and OPNSense,
    • Network management based on SNMP and the use of Zabbix as a monitoring system;
      • Reference architectures for enterprise networks and data center networks,
    • Network aspects in cloud computing

    Teaching methods

    • Lecture in seminar style, with blackboard writing and projection
    • Solving practical exercises in individual or team work

    Participation requirements

    None

    Forms of examination

    written exam paper

    Requirements for the awarding of credit points

    passed written exam

    Applicability of the module (in other degree programs)

    Master's degree in Computer Science

    Literature

    • Larry L. Peterson Bruce S. Davie: Computer Networks: a system approach, 2.ed., Morgan
      Kaufmann
    • Douglas Comer / David L. Stevens: Internetworking with TCP/IP, Vol.1 und 2, Prentice Hall

    Knowledge based systems in medicine
    • WP
    • 4 SWS
    • 6 ECTS

    • Number

      47613

    • Language(s)

      en

    • Duration (semester)

      1

    • Contact time

      60 h

    • Self-study

      120 h

    Learning outcomes/competences

    Subject and methodological competence:
    After completing the module, students will be able to name the different types of knowledge as well as their special features and differences. They will be familiar with different organizational structures for knowledge storage and will be able to evaluate their suitability for given scenarios. They will be able to gain an overview of the different types of knowledge-based methods and systems and  independently develop solution and system concepts for given practical application scenarios.
    Among other things, students will be able to

    • to name and explain the types of knowledge and their use in medicine
    • describe the structure and functioning of knowledge-based systems in general and specifically in medicine
    • to model knowledge of different types of knowledge
    • select technologies and organizational structures for computational knowledge storage and processing
    • to create concepts for the implementation of knowledge bases as well as knowledge-based and decision-supporting informatics artefacts and systems in medicine and to critically evaluate their suitability
    Social skills:
    • Collaboration in analyzing and researching new topics
    • Discussions on specific aspects of the course
    Professional field orientation:
    • Implementation competence for smaller artifacts of knowledge-based decision support in medicine

    Contents

    Principle structure of knowledge-based and decision-support systems

    • Knowledge base
    • Authoring system for knowledge engineers
    • Inference engine
    • Control module
    • Fact database
    • User interface
    • Interfaces to operational information systems
    Principle structures for knowledge storage
    • Medical information portals
    • Fact data and action repositories
    • Case collections and descriptions
    • Ontologies
    • Medical guidelines, clinical pathways and algorithms
    • Decision tables and matrices
    • Rule-based systems
    Aspects of the context-sensitive coupling of information systems and knowledge applications
    • Semantic aspects
    • EAV-based coupling
    • Mechanisms of system-internal triggering
    Machine Learning (ML)-based approaches to decision support
    • Basic functionality of ML
    • Possible applications in medicine: opportunities and risks
    • Assessment of their reliability and explainability (xAI)
    Selected applications and their implementation specifics as well as persistence structures in medicine
    • CPOE
    • AMTS applications
    • Applications to improve patient safety
    • Guideline application
    • Application of clinical pathways and algorithms
    • Laboratory diagnostics
    • Decision support systems in differential diagnostics
    • decision-supporting approaches in image processing

    Teaching methods

    • Lecture in interaction with the students, with blackboard writing and projection
    • Processing exercises during the lecture, if necessary on the computer in individual or team work with presentation
    • Active, self-directed learning through the use of electronic learning materials
    • Excursion

    Participation requirements

    None

    Forms of examination

    semester-accompanying project-related work with documentation and presentation incl. discussion, duration of presentation incl. discussion: 30 minutes

    Requirements for the awarding of credit points

    passed semester-accompanying project-related work with documentation and presentation including discussion, with a grade of 4.0 or better

    Applicability of the module (in other degree programs)

    Master's degree in Medical Informatics

    Literature

    • Acharjya, D. P., & Ma, K. (Eds.). (2024). Computational Intelligence in Healthcare Informatics. Springer.
    • Berner, E. S. (2007). Clinical decision support systems (Vol. 233). New York: Springer Science+ Business Media, LLC.
    • Hunink, M. M., Weinstein, M. C., Wittenberg, E., Drummond, M. F., Pliskin, J. S., Wong, J. B., & Glasziou, P. P. (2014). Decision making in health and medicine: integrating evidence and values. Cambridge university press.
    • Penman, I. D., Ralston, S. H., Strachan, M. W., & Hobson, R. (Eds.). (2022). Davidson's Principles and Practice of Medicine E-Book: Davidson's Principles and Practice of Medicine E-Book. Elsevier Health Sciences.
    • Sackett, D. L. (1997, February). Evidence-based medicine. In Seminars in perinatology (Vol. 21, No. 1, pp. 3-5). WB Saunders.
    • Simon, G. J., & Aliferis, C. (2024). Artificial intelligence and machine learning in health care and medical sciences: best practices and pitfalls.
    • Topol, E. (2019). Deep medicine: how artificial intelligence can make healthcare human again. Hachette UK.
    • Warner R., Sorensen D. K., Bouhaddou O.: Knowledge Engineering in Health Informatics. Springer New York 1997.

    Machine Learning
    • WP
    • 4 SWS
    • 6 ECTS

    • Number

      46839

    • Language(s)

      en

    • Duration (semester)

      1

    • Contact time

      60 h

    • Self-study

      120 h

    Learning outcomes/competences

    The course deals with the development and analysis of machine learning methods in applications of computer science, medical informatics and general information systems. After successfully completing the course, students will have acquired the following skills:

    Knowledge (knowledge):
    - know the most important technical terms of machine learning and can use them to explain learning systems.
    - They can implement and evaluate the use of machine learning methods for their own application tasks. To this end, students are familiar with typical applications of these methods.
    - know project management methods for machine learning applications such as CRISP-DM
    - know explanatory components for machine learning and can interpret the results
    - know typical problems of machine learning such as overfitting and information leakage and can avoid them
    - know the theoretical limits of machine learning systems and can describe these formally and use them to estimate the limits of their own applications
    .

    Skills
    - can design, implement and analyze machine learning systems for specific application contexts in computer science.
    - can question and discuss the ethical foundations and limitations of machine learning systems
    - can select suitable machine learning methods for applications
    - can implement simple deep learning solutions with JupyterHub
    - can plan, implement, analyze and document industry-related mini-projects independently in a team

    Competencies (personal and social skills)
    - can formulate ideas and proposed solutions orally and in writing
    - can solve tasks in exercises and practicals independently and present the results
    - can independently develop and present theoretical content on the topic of machine learning from scientific literature
    - can develop solutions cooperatively in the exercise and project phases
    - can cooperatively plan, distribute and jointly carry out tasks for solutions in the project phases
    - can argue in discussions in a goal-oriented manner and deal with criticism objectively
    - can present the results of group work together
    - can evaluate project results and formulate suggestions for improvement
    - can recognize and reduce misunderstandings between discussion partners


    Contents

    - Basic concepts of machine learning
    - Use of KNime for machine learning
    - Design of evaluation studies for machine learning methods and implementation of such studies
    - Linear models
    - Different models of supervised and unsupervised neural networks
    - Learning methods for structured data: support vector machine, decision trees, random forest, gradient boosting machines (GBM)
    - Nearest neighbor methods, lazy learning and embeddings
    - Bayesian networks
    - Unsupervised learning methods (k-means)
    - Combination models (ensembles, boosting machines)
    - Deep Learning models (Convolutional Neural Networks (CNN), Long Short Term Memory (LSTM), Transformer architectures e.g. BERT, Visual Transformer)
    - Deep learning concepts - transfer learning, data augmentation, Generative Adversarial Networks (GAN)
    - Deep learning - parallelization with GPUs, implementation on mobile platforms with low resources
    - Large language models and use of embeddings, Retrieval Augmented Generation (RAG)
    - Theoretical concepts: Bias-Variance Dilemma, No Free Lunch Theorem
    - Explainability of models
    - Applications with data from different modalities (text, image, sound), Word2Vec, FastText, Transformer
    - Methods for improving generalization performance (regularization, feature selection, dimension reduction, complexity adjustment)
    - Problem solving using the example of course-related mini-projects from industrial applications (student mini-projects in teams of 2-3)

    Teaching methods

    • Lecture in seminar style, with blackboard and projection
    • Processing programming tasks on the computer in individual or team work
    • Project work accompanying the lecture with final presentation
    • Inverted teaching (inverted classroom)

    Participation requirements

    None

    Forms of examination

    • written examination paper (70% of the examination performance)
    • examinations during the semester (30% of the examination performance)

    Requirements for the awarding of credit points

    • passed written examination
    • successful examination performance during the semester (mini-project with presentation)

    Applicability of the module (in other degree programs)

    • Master of Computer Science
    • Master's degree in Medical Informatics
    • Master's degree in Business Informatics
    • Master's degree in Digital Transformation
    • Master of Embedded Systems Engineering
    • Master's degree in Medical Informatics

    Literature

    - I. Witten, E. Frank, M. Hall und C. J. Pal, Data Mining: Practical Machine Learning Tools and Techniques, 4. Auflage, Morgan Kaufmann (2017) - elektronische Version im Intranet verfügbar
    - C. M. Bishop, Pattern Recognition and Machine Learning, Springer (2006)
    - E. Alpaydin, Introduction to Machine Learning (Adaptive Computation and Machine Learning), Revised and Updated Edition, MIT Press (2021)
    - I. Goodfellow, Y. Bengio und A. Courville: Deep Learning, MIT Press (2016)

    Requirements Engineering
    • WP
    • 4 SWS
    • 6 ECTS

    • Number

      46910

    • Language(s)

      en

    • Duration (semester)

      1

    • Contact time

      60 h

    • Self-study

      120 h

    Learning outcomes/competences

    After successfully completing the module, students will be able to:

    Knowledge and understanding:
    • explain the role of requirements engineering (RE) in the context of today's challenges in projects, e.g. the "VUCA world" (Volatility, Uncertainty, Complexity, Ambiguity)
    • combining various creativity and elicitation techniques appropriately to determine requirements, as well as documenting and validating requirements
    • to explain how different activities in RE can be supported with generative AI and where the limits lie


    Deployment, application and generation of knowledge:

    • Fit an RE process into an organization and select procedures, techniques and tools in RE according to the framework conditions of a project.
    • Elicit, analyze, specify and validate requirements in a goal-oriented manner.
    • Deal with specific requirements issues such as variability and adaptivity.

    Communication and collaboration:

    • To develop techniques and methods in RE yourself and to teach them to others
    • Communicate effectively with various stakeholders, e.g. customers, developers and end users, to develop, refine and validate requirements
    • Collaborate in teams to develop concepts and solutions while balancing different perspectives and interests

    Scientific self-image / professionalism:

    • Build and leadRE expertise in a cross-domain team
    • Recognize the ethical and professional responsibilities associated with translating stakeholder needs into successful systems
    • Critically evaluate practices in RE and identify improvements

    Contents

    Requirements engineering (RE) is the first activity in software, system and service development. Requirements are the starting point for design, verification and validation as well as for the testing and integration of systems. Configuration and change request management are associated with RE. The definition of requirements and the structured handling of requirements continues to be an important area for research into tools and methods - especially for large and complex mechatronic systems. In this module, students will gain specific knowledge about the current state of the art and the most important future challenges in the field of RE. 
     
    • Introduction to requirements engineering
      • Definition, relevance and challenges
      • Embedding depending on system type and project characteristics
      • Frameworks (e.g. Jackson's WRSPM model)
    • Requirements determination
      • Stakeholder identification
      • Creativity and innovation
      • Interviews, focus groups and ethnography
      • Brainstorming and collaborative workshops
    • Requirements documentation
      • Standards for requirements specifications (SRS)
      • Informal methods: prototypes, storyboards
      • Modeling requirements: i-star, UML, user stories
      • Tools: JIRA, Confluence, ReqIF
    • Validation and verification
      • Quality characteristics: Completeness, consistency, accuracy
      • Prototyping and user feedback
      • Strategies for testing requirements
    • Requirements management
      • Prioritization techniques: e.g. MoSCoW, Kano, weighted scoring
      • Traceability
      • Impact analysis for changes
      • Versioning and change management
      • Improvement of the RE process
    • Advanced topics
      • Software product lines, adaptive systems and crowd-based systems
      • Domain-specific languages
      • Generative AI and natural language processing in RE

    Teaching methods

    Lecture with blackboard and projection

    Participation requirements

    None

    Forms of examination

    • written examination paper, duration 90 min (70%)
    • semester-accompanying examination, as 

      project-related work with presentation

       (30%)

    Requirements for the awarding of credit points

    • passed written examination
    • successful mini-project (project-related work)

    Applicability of the module (in other degree programs)

    • Master of Computer Science
    • Master's degree in Medical Informatics
    • Master's degree in Business Informatics

    Literature

    • Klaus Pohl. Requirements Engineering: Fundamentals, Principles and Techniques. Springer, 2025
    • Klaus Pohl und Chris Rupp: Basiswissen Requirements Engineering: Aus- und Weiterbildung nach IREB-Standard zum Certified Professional for Requirements Engineering Foundation Level, 2015
    • Brian Berenbach, Daniel Paulish, Juergen Kazmeier, Arnold Rudorfer. Software and Systems Requirements Engineering In Practice, McGraw-Hill, March 2009
    • Klaus Pohl, Günter Böckle und Frank J. van der Linden. Software Product Line Engineering: Foundations, Principles and Techniques, Springer, Januar 2011
    • Søren Lausen. Software Requirements - Styles and Techniques, Addison-Wesley, 2002.
    • Ellen Gottesdiener. Requirements by Collaboration - Workshops for Defining Needs. Addison-Wesley, 2002

     

    Selected Aspects of Information Security
    • WP
    • 4 SWS
    • 6 ECTS

    • Number

      46857

    • Language(s)

      en

    • Duration (semester)

      1

    • Contact time

      60 h

    • Self-study

      90 h

    Learning outcomes/competences

    The students are able to define
    relevant terms. - define, differentiate and explain relevant terms.
    - understand the crucial importance of standardization in information security and implement it methodically.
    - apply practical methods, best practices and software tools.
    - implement project tasks independently and document results.

    Contents

    - Depending on the topics actually selected for the respective semester.
    - Exemplary topics:
      - Information security management systems: basics, ISO/IEC 27000 series, threat modeling, risk management
      - Operating system security: Capabilities, AppArmor, SELinux, Linux hardening
      - Network security: firewall systems, intrusion detection/prevention systems (IDS/IPS)
      - Software security: penetration testing, static application security testing (SAST)
      - Hardware security: CPU Security, Trusted Platform Module (TPM), Smartcards
      - Further topics: Privacy, biometric systems

    The language of instruction is English.

    Teaching methods

    - Lecture in interaction with the students, with blackboard writing and projection
    - Individual work
    - Project work accompanying the lecture with final presentation

    Participation requirements

    None

    Forms of examination

    - Project-related work (100%)

    Requirements for the awarding of credit points

    - Successful project work

    Applicability of the module (in other degree programs)

    • Master of Computer Science
    • Master's degree in Medical Informatics
    • Master's degree in Business Informatics

    Importance of the grade for the final grade

    5,00%

    Literature

    - Abhängig von den für das jeweilige Semester tatsächlich ausgewählten Themen.

    Usability Engineering
    • WP
    • 4 SWS
    • 6 ECTS

    • Number

      46908

    • Language(s)

      en

    • Duration (semester)

      1

    • Contact time

      60 h

    • Self-study

      120 h

    Learning outcomes/competences


    Knowledge and understanding
    After successfully completing the module, students will be able to
    • systematically develop design solutions and test their suitability for use
    • create, carry out and evaluate research designs


    Use, application and generation of knowledge
    After successfully completing the module, students will be able to
    • make methodological considerations and justify selections

    Communication and cooperation
    Through successful participation in the module, students become
    • plan and implement their own research project as a small group
    • practice the procedure in a joint project


    Scientific self-image / professionalism
    After successfully completing the module, students will be able

    • to reflect on the methodology of their subject area and beyond and to examine specific works for their methodology

    Contents

    1. Basic concepts of usability
    2. Relations and links to human-computer interaction
    3. Usability engineering - early approaches
    4. Study design and evaluation methods
    5. Project work
    6. Special chapter: Usability in relation to a current research topic

       

    Teaching methods

    Seminar-style lecture with presentation, group work and assignments

    Participation requirements

    None

    Forms of examination

    Term paper with presentation (80%)
    Semester performance through work assignments (20%)

    Requirements for the awarding of credit points

    Total passed performance from
    • Thesis and presentation (80%)
    • Semester accompanying performance (20%)

    Applicability of the module (in other degree programs)

    • Master of Computer Science
    • Master's degree in Medical Informatics
    • Master's degree in Business Informatics
    • Master's degree in Digital Transformation
    • Master of Embedded Systems Engineering
    • Master's degree in Medical Informatics

    Literature

    Dix, A., Finlay, J., Abowd, G., Beale, R. (2004): Human-Computer Interaction. Pearson Education Limited.
    DIN EN ISO 9241
    Wilde, T., Hess, T. (2007): Forschungsmethoden der Wirtschaftsinformatik. Wirtsch. Inform. 49, 280–287.
    Aktuelle Forschungspapiere zum Thema Usability und User Experience
    Standardisierte Fragebogeninstrumente zum Thema Usability und User Experience

    3. Semester of study

    MI Teamproject
    • PF
    • 2 SWS
    • 6 ECTS

    • Number

      47641

    • Language(s)

      en

    • Duration (semester)

      1

    • Contact time

      30 h

    • Self-study

      150 h

    Learning outcomes/competences

    The event is a team project with 5-12 participants in which a scientific project in medical informatics is planned and developed together. Project mentors from medical informatics companies or practice partners from clinics (e.g. Essen University Hospital) are involved to provide support. After successful participation in the course, students will have acquired the following skills:


    Knowledge (knowledge):
    - know the methods of qualified literature research
    - know methods for the selection of software frameworks
    - know the current state of research of the selected project

    Skills
    - can formulate and present intermediate and final results in an understandable way
    - can apply methods of medical software and hardware development in a scientific context
    - can work scientifically in a team on a given topic from the context of current research in medical informatics
    - can design experiments to demonstrate the performance of the project solution
    - can classify and present the state of the art for their chosen topic and find their own approaches for further development
    .

    Competencies (personal and social skills)
    - can master the organization and development of a scientific project together (students organize the distribution of tasks and review independently)
    - can formulate ideas and proposed solutions orally and in writing
    - can cooperatively plan, distribute and jointly carry out tasks for solutions in the project phases
    - can argue in discussions in a goal-oriented manner and deal with criticism objectively
    - can present the results of group work together
    - can evaluate project results and formulate suggestions for improvement
    - can recognize and resolve misunderstandings between discussion partners

    Contents

    On the basis of a brief introductory presentation of the application or area of application of the project, tasks are assigned for independent work by the team, which are to be carried out in accordance with good scientific practice and should also include a more complex software implementation. Ideally, the result will lead to a joint scientific publication.

    Teaching methods

    • Group work
    • Project work
    • Independent scientific processing

    Participation requirements

    None

    Forms of examination

    • Project presentation and submission of documentation and software artifacts

    Requirements for the awarding of credit points

    • Successful group presentation and submission of software artifacts. Individual grading according to the equally weighted criteria: Teamwork, software contribution, management contribution, documentation contribution, presentation 

    Applicability of the module (in other degree programs)

    Master's degree in Medical Informatics
    Master Medical Informatics

    Importance of the grade for the final grade

    5,00%

    Literature

    Literatur muss von den Studierenden selbst in Bezug zum gewählten Thema ermittelt werden.

    Research Project
    • PF
    • 4 SWS
    • 12 ECTS

    • Number

      47652

    • Language(s)

      en

    • Duration (semester)

      1

    • Contact time

      60 h

    • Self-study

      360 h

    Learning outcomes/competences

    Knowledge
    - Knows the state of the art in a specific scientific field
    - Knows open research questions in this field
    - Knows the relevant literature
    - Knows the methodology and tools for carrying out the project

    Skills
    - Can define and plan own research project
    - Can apply a suitable research methodology
    - Can produce own research results
    - Can describe project implementation, methodology and results in a scientific report
    - Can write a research paper on the chosen topic

    Competence - (self and social competence)
    - Can carry out their own, more complex scientific research project
    - Can master uncertainties and unfamiliar topics in a new field
    - Can present and defend results (in a colloquium or at a conference)

    Contents

    The research project is intended to introduce students to scientific research work in a wider context. The students will participate in one of the ongoing research projects. They will participate with their own sub-project. The starting point is the definition of the research questions they want to answer and the selection of the appropriate methodology. Students plan their project independently and carry it out under regular supervision and counseling. They summarize their results in a research project paper (project report). The research project serves as preparation for further work on the Master's thesis. The aim of the research project is to familiarize the student with research methodology in a specific scientific field and to formulate the scientific status and research questions. The student demonstrates the ability to conduct their own and independent research at Master's level and with a certain degree of complexity.


    Structure of the course

    Students choose a topic from one of the ongoing projects or a topic from Medical Informatics at Essen University Hospital. They receive individual counseling and feedback. During the semester, students write a project paper and present it in a colloquium at the end of the semester.

    Outstanding results should be published and presented at a conference (orally or as a poster) (can also be done in conjunction with the Master's thesis).


    Occupational field orientation

    The research project (thesis) is carried out in connection with a research project in Medical Informatics. It is recommended that the project and the thesis are carried out as part of an internship/student job in medical informatics or as part of a research project at a university or research institute.

    Teaching methods

    Project work is carried out with individual supervision:

    - Project work, in a scientific project or as part of an internship in industry
    - Writing a scientific report
    - Presentations to communicate and discuss the results
    - Individual assessment and feedback on work and presentations

     

    Participation requirements

    None

    Forms of examination

    Written project elaboration
    colloquium

    Requirements for the awarding of credit points

    Assessed project work (80%) and passed (20%)

    Applicability of the module (in other degree programs)

    Depending on choice of courses

    Importance of the grade for the final grade

    5,00%

    Literature

    Muss vom Studierenden abhängig vom Projekt recherchiert werden

    4. Semester of study

    Masters Thesis and Colloquium
    • PF
    • 0 SWS
    • 30 ECTS

    • Number

      103

    • Language(s)

      en

    • Duration (semester)

      1

    Notes and references

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