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Biomedical subjects

Udo Hahn

Publications and source records attributed to Udo Hahn.

3 recordsLinked to original sources

MEDSYNDIKATE--a natural language system for the extraction of medical information from findings reports.

MEDSYNDIKATE is a natural language processor, which automatically acquires medical information from findings reports. In the course of text analysis their contents is transferred to conceptual representation structures, which constitute a corresponding text knowledge base. MEDSYNDIKATE is particularly adapted to deal properly with text structures, such as various forms of anaphoric reference relations spanning several sentences. The strong demands MEDSYNDIKATE poses on the availability of expressive knowledge sources are accounted for by two alternative approaches to acquire medical domain knowledge (semi)automatically. We also present data for the information extraction performance of MEDSYNDIKATE in terms of the semantic interpretation of three major syntactic patterns in medical documents.

Confidence Intervals↗

Creating knowledge repositories from biomedical reports: the MEDSYNDIKATE text mining system.

MEDSYNDIKATE is a natural language processor for automatically acquiring knowledge from medical finding reports. The content of these documents is transferred to formal representation structures which constitute a corresponding text knowledge base. The system architecture integrates requirements from the analysis of single sentences, as well as those of referentially linked sentences forming cohesive texts. The strong demands MEDSYNDIKATE poses to the availability of expressive knowledge sources are accounted for by two alternative approaches to (semi)automatic ontology engineering. We also present data for the knowledge extraction performance of MEDSYNDIKATE for three major syntactic patterns in medical documents.

Confidence Intervals↗

A knowledge representation view on biomedical structure and function.

In biomedical ontologies, structural and functional considerations are of outstanding importance, and concepts which belong to these two categories are highly interdependent. At the representational level both axes must be clearly kept separate in order to support disciplined ontology engineering. Furthermore, the biaxial organization of physical structure (both by a taxonomic and partonomic order) entails intricate patterns of inference. We here propose a layered encoding of taxonomic, partonomic and functional aspects of biomedical concepts using description logics.

Artificial Intelligence↗