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A Neuro-ontology for the neurological examination.
BMC Medical Informatics and Decision Making ( IF 3.5 ) Pub Date : 2020-03-04 , DOI: 10.1186/s12911-020-1066-7
Daniel B Hier 1 , Steven U Brint 1
Affiliation  

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.

中文翻译:

用于神经学检查的神经本体论。

背景技术在电子健康记录中用于机器学习或数据分析的临床数据的使用取决于将自由文本转换为机器可读代码。我们已经检查了基于UMLS Metathesaurus概念将神经系统检查捕获为机器可读代码的可行性。方法我们使用UMLS Metathesaurus的1100个概念创建了用于捕获神经系统检查的目标本体。我们基于419个已发布的神经科病例创建了2386个测试短语的数据集。然后,我们将测试短语映射到目标本体。结果我们能够将所有2386个测试短语映射到601个独特的UMLS概念。具有1100个概念的神经系统检查本体具有足够的广度和覆盖范围,可以对从419个测试用例得出的所有神经系统概念进行编码。仅使用预先协调的概念,UMLS的组件本体(例如HPO,SNOMED CT和OMIM)没有足够的深度和广度来编码神经系统检查的复杂性。结论基于UMLS子集的本体具有足够的广度和覆盖深度,可以使用预先协调的概念将神经系统检查的缺陷转换为机器可读的代码。将一小部分UMLS概念用于神经系统检查本体提供了改进的可管理性以及整理层次结构和包含关系的机会的优势。结论基于UMLS子集的本体具有足够的广度和覆盖深度,可以使用预先协调的概念将神经系统检查的缺陷转换为机器可读的代码。将一小部分UMLS概念用于神经系统检查本体提供了改进的可管理性以及整理层次结构和包含关系的机会的优势。结论基于UMLS子集的本体具有足够的广度和覆盖深度,可以使用预先协调的概念将神经系统检查的缺陷转换为机器可读的代码。将一小部分UMLS概念用于神经系统检查本体提供了改进的可管理性以及整理层次结构和包含关系的机会的优势。
更新日期:2020-04-22
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