Transforming clinical and clinical research data to ontology-driven linked data
In the daily practice of a biomedical informaticist with a specialization requests for transforming pre-existing, mostly tabular, data to data that can be curated and consumed using Semantic Web Technologies (SWT) are still quiet common. The reasons for these request differ from project to project to project, but they are certainly due to the advantages of semantic web technologies when it comes to semantic interoperability and the schema-free data presentation, which fosters maintainability and flexibility. The request and the expectations of how SWT data created from a given data set should clearly, often demonstrate a lack of appreciation of the level of difference between the strategies of how data is represented in tabular ways and in SWT. In this course we will discuss concrete examples of how the results of a data transformation from tabular data, using for example common data elements, to SWT-based data for clinical and clinical research data ought to look. We will focus on the representational aspects over questions of tooling in this course.
The Use of Axiomatically-Rich Ontologies in Biomedical Research
There exists a communication gap between the biomedical informatics community and the computer science/artificial intelligence community regarding the terms “semantics”, “semantic integration”, and “knowledge representation”. This communication gap has led to wide-spread uptake clinical terminologies and common data models (CDM) promising to provide semantics, semantic integration”, and knowledge representation. Recently, the demand for integrating and analyzing increasingly large data sets in clinical and translational research has led to numerous efforts to harmonize existing CDMs and integrate data curated based on those models. These efforts raise the question of how to appropriately represent the semantics of data. The question of how to formally assure that mappings between CDMs are correct is often overlooked. The answer to these challenges lies in using axiomatically-rich ontologies that allow verifying that terms refer to the same set of entities using automatic inference. This verification is only possible by building ontologies that represent the content of the scientific disciplines in accordance with the reality of the domain of the disciplines. The presentation will explore how a strong logical representation of the scientific domain does not only foster harmonization of CDMs, but also informs and facilitates the transition from data over information to knowledge and thus, support meaningful use.
About the speaker
Mathias Brochhausen is Associate Professor at the Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA. In addition, he serves as the Associate Education Director of this Department. Before joining the University of Florida he was Associate Professor and Vice-Chair for faculty development in the Department of Biomedical Informatics at the University of Arkansas for Medical Sciences in Little Rock, Arkansas, United States. He received a Ph.D. in Philosophy from Johannes Gutenberg-University, Mainz, Germany in 2004. Before joining UAMS in 2011, he was researcher at and manager of the Institute for Formal Ontology and Medical Information Science and executive director of the European Centre for Ontological Research, both at Saarland University in Saarbrücken, Germany. His research interests include semantic technologies, particularly knowledge representation and reasoning applied to clinical and clinical research data. Brochhausen developed and co-developed multiple ontologies coded in Web Ontology Language (OWL), such as the Document Act Ontology (d-acts), the Ontology for Biobanking (OBIB) the Drug Ontology (DRON), Ontology of Biomedical Investigations (OBI), etc. He is currently completing work on the Ontology of Organizational Structures of Trauma systems and Trauma centers (OOSTT) as part of the Comparative Assessment Framework for Environments of Trauma Care (CAFÉ) project and continues to contribute to the Drug-drug Interaction and Drug-drug Evidence Ontology (DIDEO). He is beginning work on a new project to investigate patterns of temporal disease progression. He is the author of over 40 peer-reviewed publications, is an associate editor of BMC Medical Informatics and Decision Making, has refereed over a dozen journals, and has served on numerous conference program committees.