A Neuro-ontology for the neurological examination

BMC Medical Informatics and Decision Making
Daniel B Hier, Steven U Brint

Abstract

The use of clinical data in electronic health records for machine-learning or data analytics depends on the conversion of free text into machine-readable codes. We have examined the feasibility of capturing the neurological examination as machine-readable codes based on UMLS Metathesaurus concepts. We created a target ontology for capturing the neurological examination using 1100 concepts from the UMLS Metathesaurus. We created a dataset of 2386 test-phrases based on 419 published neurological cases. We then mapped the test-phrases to the target ontology. We were able to map all of the 2386 test-phrases to 601 unique UMLS concepts. A neurological examination ontology with 1100 concepts has sufficient breadth and depth of coverage to encode all of the neurologic concepts derived from the 419 test cases. Using only pre-coordinated concepts, component ontologies of the UMLS, such as HPO, SNOMED CT, and OMIM, do not have adequate depth and breadth of coverage to encode the complexity of the neurological examination. An ontology based on a subset of UMLS has sufficient breadth and depth of coverage to convert deficits from the neurological examination into machine-readable codes using pre-coordinated concepts. The use of a small sub...Continue Reading

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Citations

Aug 28, 2020·BMC Medical Informatics and Decision Making·Daniel B HierBlaine Allen
Aug 5, 2021·BMC Medical Informatics and Decision Making·Hetong MaQing Qian
Sep 4, 2021·Yearbook of Medical Informatics·Ferdinand DhombresUNKNOWN Section Editors for the IMIA Yearbook Section on Knowledge Representation and Management
Sep 29, 2021·Methods of Information in Medicine·Anna RossanderDaniel Karlsson

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Software Mentioned

SNOMED
MEDCIN
OMIM
MRCONSO
UMLS
Protégé
SNOMED CT Browser
Shrimp Ontoserver
CT
UMLS browser

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