A Neuro-ontology for the neurological examination
Abstract 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 Meta...
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| Format: | Article |
| Language: | English |
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BMC
2020-03-01
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| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12911-020-1066-7 |
| _version_ | 1818190373461164032 |
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| author | Daniel B. Hier Steven U. Brint |
| author_facet | Daniel B. Hier Steven U. Brint |
| author_sort | Daniel B. Hier |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-11T23:57:41Z |
| format | Article |
| id | doaj.art-06aff081fd6843a3bab4ac8352920cac |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-12-11T23:57:41Z |
| publishDate | 2020-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-06aff081fd6843a3bab4ac8352920cac2022-12-22T00:45:19ZengBMCBMC Medical Informatics and Decision Making1472-69472020-03-012011910.1186/s12911-020-1066-7A Neuro-ontology for the neurological examinationDaniel B. Hier0Steven U. Brint1Department of Neurology and Rehabilitation, University of Illinois at ChicagoDepartment of Neurology and Rehabilitation, University of Illinois at ChicagoAbstract 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.http://link.springer.com/article/10.1186/s12911-020-1066-7UMLS MetathesaurusOntologyNeurological examinationElectronic health recordsSNOMED CT |
| spellingShingle | Daniel B. Hier Steven U. Brint A Neuro-ontology for the neurological examination BMC Medical Informatics and Decision Making UMLS Metathesaurus Ontology Neurological examination Electronic health records SNOMED CT |
| title | A Neuro-ontology for the neurological examination |
| title_full | A Neuro-ontology for the neurological examination |
| title_fullStr | A Neuro-ontology for the neurological examination |
| title_full_unstemmed | A Neuro-ontology for the neurological examination |
| title_short | A Neuro-ontology for the neurological examination |
| title_sort | neuro ontology for the neurological examination |
| topic | UMLS Metathesaurus Ontology Neurological examination Electronic health records SNOMED CT |
| url | http://link.springer.com/article/10.1186/s12911-020-1066-7 |
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