A Neuro-ontology for the neurological examination

Autor: Steven U. Brint, Daniel B. Hier
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Neurological examination
UMLS Metathesaurus
Computer science
Health Informatics
02 engineering and technology
Ontology (information science)
computer.software_genre
ENCODE
SNOMED CT
lcsh:Computer applications to medicine. Medical informatics
Health informatics
03 medical and health sciences
Component (UML)
0202 electrical engineering
electronic engineering
information engineering
medicine
Humans
Electronic health records
030304 developmental biology
Neurologic Examination
0303 health sciences
medicine.diagnostic_test
business.industry
Ontology
Health Policy
Unified Medical Language System
Systematized Nomenclature of Medicine
Computer Science Applications
Test case
Biological Ontologies
lcsh:R858-859.7
020201 artificial intelligence & image processing
Artificial intelligence
business
computer
Natural language processing
Research Article
Zdroj: BMC Medical Informatics and Decision Making, Vol 20, Iss 1, Pp 1-9 (2020)
BMC Medical Informatics and Decision Making
ISSN: 1472-6947
DOI: 10.1186/s12911-020-1066-7
Popis: Background 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. Methods 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. Results 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. Conclusion 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 subset of UMLS concepts for a neurological examination ontology offers the advantage of improved manageability as well as the opportunity to curate the hierarchy and subsumption relationships.
Databáze: OpenAIRE
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