Just the Right Amount: SNOMED CT Content Extraction

Dr.-Ing. Renate Schmidt, University of Manchester

Abstract

SNOMED CT is established technology of AI in health, where it provides the basis for medical terminological services used to support consistent data capture, easy data sharing and convenient analysis of data. SNOMED CT is a large knowledge base (ontology) of definitions of medical codes used by clinicians in health care sectors worth-wide. After a brief introduction of medical ontologies and their benefits, this talk will review subontologies, a bespoke technique for procuding concise extracts of SNOMED CT, their key features, use cases, successful results and their development in a successful collaboration with industry.

Speaker Bio

Renate Schmidt is University Reader in Computer Science and Leader of Formal Methods Research Group in the Department of Computer Science at the University of Manchester. She served as Chair of the PGR Degrees Panel in the Faculty of Science and Engineering and was Member of the FSE Doctoral Academy Academic Leadership Team and the Faculty Graduate Committee (2021-2024). She is Associate Editor or Editorial Board Member of Artificial Intelligence Journal, Journal of Artificial Intelligence Research, Journal of Automated Reasoning and Journal of Applied Non- Classical Logic. Her research involves the development of both theoretical results and implemented systems for knowledge representation, automated symbolic reasoning and formal methods. Her current research is driven by the aim to develop improved automated support for knowledge representation, ontology extraction, knowledge re- engineering, information hiding/obfuscation, abductive learning and query answering in the context of ontologies.

Time & Place

Wednesday, July 23, 2025
10:30 – 12:00
Reisensburg Castle

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