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Semantic Analyses of Medical Data Models

Understanding semantic interoperability and generation of common data elements in medicine

Faculty of Medicine | University of Münster (WWU)

This module is part of the certificate Management and Analysis of Medical Data

Teaching Language: German

Workload: 0h presence / 20h online / 160h self-study = 180h total1 (6 ECTS)

Target Group: Students of computer science, medical informatics or information systems, who are interested in obtaining skills in the generation of harmonized and efficient data models for electronic data capture in research registers or clinical routine documentation. Medical students (writing their MD thesis or selecting an elective module), physicians (continuing education) are welcome as well.

Pretty young student studying at home sitting at her dining table with a large binder of notes checking something on the screen of her laptop computer
top view of Medicine doctor hand working with modern computer and smart phone on wooden desk as medical concept-1
Focused classmates studying together and using laptop in library

Consultation & Registration:
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Form of teaching: Lecture with learning tasks, short presentation and final oral exam  

Missing semantic annotations and heterogeneous data definitions impede cross-institutional data integration both for research and routine care applications.

Participants of this module will acquire skills to identify and tackle current semantic challenges in medical data integration and will develop competencies to analyze existing medical data models for the generation of interoperable common data elements or core data sets within any disease domain.

To achieve this, organizational and technical aspects of implementing core data will be covered by illustrating real-world implementations of hospital information systems.

Learning Objectives

Developing practical skills for semantic analyses of medical data models and the generation of common data elements in different disease domains. The module will cover the concepts of semantic interoperability, research data standards such as the Operational Data Model by the Clinical Data Interchange Standards Consortium (CDISC ODM), metadata standards such as the ISO 11179.

In addition, practical skills for using medical terminology systems such as the Unified Medical Language System, medical coding principles and semantic analyses using well-established tools (Varghese et al., 2018) will be acquired. Based on the FAIR (Findable, Accessible, Interoperable, Reusable) guiding principles for scientific data management, the participants will be familiarized with a metadata platform for finding, accessing, creating interoperable and reusable medical data models to generate harmonized data elements.



Varghese, J., Fujarski, M., Hegselmann, S., Neuhaus, P., & Dugas, M. (2018). CDEGenerator: an online platform to learn from existing data models to build model registries. Clinical epidemiology, 10, 961–970.


1The times serve as rough orientation. The real times may differ.