Seminar Semantic Analyses of Medical Data Models
Motivation:
Missing semantic annotations and heterogeneous data definitions impede cross-institutional data integration both for research and routine care applications. Participants of this seminar 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 sets 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 seminar 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 (UMLS), medical coding principles and semantic analyses using well-established tools 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.
Workflow:
A scientific publication will be assigned to each participant that illustrates one aspect of developing and implementing a core dataset for a given disease domain. At the end of the semester, a seminar thesis about the assigned paper must be provided and a presentation about the paper must be given. Since each participant will only focus on a single aspect of creating a core dataset, additional material and optional exercises are provided as eLearning self-study to deepen the understanding of the entire workflow.