ArticleBMC medical informatics and decision making2020
A Neuro-ontology for the neurological examination.
Article in BMC medical informatics and decision making, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- A formal explanation space for the simultaneous clustering of neurologic diseases based on their signs and symptoms.BMC medical informatics and decision making · 2025Article
- A knowledge-based clinical decision support system for personalized health examination items in China: design and evaluation.BMC medical informatics and decision making · 2025Article
- AI for Analyzing Mental Health Disorders Among Social Media Users: Quarter-Century Narrative Review of Progress and Challenges.Journal of medical Internet research · 2024Review
- Inter-rater agreement for the annotation of neurologic signs and symptoms in electronic health records.Frontiers in digital health · 2023Article
- The visualization of Orphadata neurology phenotypes.Frontiers in digital health · 2023Article
- Enhanced neurologic concept recognition using a named entity recognition model based on transformers.Frontiers in digital health · 2022Article
- Subtypes of relapsing-remitting multiple sclerosis identified by network analysis.Frontiers in digital health · 2022Article
- A State-of-the Art Review of SNOMED CT Terminology Binding and Recommendations for Practice and Research.Methods of information in medicine · 2021Article
- COVID term: a bilingual terminology for COVID-19.BMC medical informatics and decision making · 2021Article
- Knowledge Representation and Management: Interest in New Solutions for Ontology Curation.Yearbook of medical informatics · 2021Review
- An automated method to enrich consumer health vocabularies using GloVe word embeddings and an auxiliary lexical resource.PeerJ. Computer science · 2021Article
- Evaluation of standard and semantically-augmented distance metrics for neurology patients.BMC medical informatics and decision making · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 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.
methodsWe 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.
resultsWe 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.
conclusionAn 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.