A Neuro-ontology for the neurological examination

Hier, Daniel B.; Brint, Steven U.

doi:10.1186/s12911-020-1066-7

Cited by 28 publications

(49 citation statements)

References 29 publications

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“…Neurological signs and symptoms that reside in the electronic health record as unstructured data can be represented as sets of UMLS concepts and stored as UMLS CUI machine readable codes. Inter-concept distances for signs and symptoms can be calculated based on a concept hierarchy [11]. Using a semantic weighted bipartite matching metric, inter-patient distances can be calculated for neurological cases.…”

Section: Discussionmentioning

confidence: 99%

“…Both terminologies assign unique machine-readable codes to a concept. We have identified 1167 core concepts from the UMLS Metathesaurus as a basis for capturing the signs and symptoms of the neurological examination [11].…”

Section: Background and Related Workmentioning

confidence: 99%

“…We calculated distances between signs and symptoms by the method of Wu and Palmer [16] utilizing the subsumption hierarchy in the neuro-ontology [11]. Distances between concepts were normalized with a minimum of 0.0 (closest) and a maximum of…”

Section: Distance Between Signs and Symptoms (Inter-concept Distances)mentioning

confidence: 99%

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A patient distance metric for neurology

Hier

Kopel

Brint

et al. 2020

Preprint

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Objective: Neurologists lack a metric for measuring the distance between neurological patients. When neurological signs and symptoms are represented as neurological concepts from a hierarchical ontology and neurological patients are represented as sets of concepts, distances between patients can be represented as inter-set distances.Methods:We converted the neurological signs and symptoms from 721 published neurology cases into sets of concepts with corresponding machine-readable codes. We calculated inter-concept distances based a hierarchical ontology and we calculated inter-patient distances by semantic weighted bipartite matching. We evaluated the accuracy of a k-nearest neighbor classifier to allocate patients into 40 diagnostic classes.Results:Within a given diagnosis, mean patient distance differed by diagnosis, suggesting that across diagnoses there are differences in how similar patients are to other patients with the same diagnosis. The mean distance from one diagnosis to another diagnosis differed by diagnosis, suggesting that diagnoses differ in their proximity to other diagnoses. Utilizing a k-nearest neighbor classifier and inter-patient distances, the risk of misclassification differed by diagnosis.Conclusion:If signs and symptoms are converted to machine-readable codes and patients are represented as sets of these codes, patient distances can be computed as an inter-set distance. These patient distances given insights into how homogeneous patients are within a diagnosis (stereotypy), the distance between different diagnoses (proximity), and the risk of diagnosis misclassification (diagnostic error).

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Section: Discussionmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

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A patient distance metric for neurology

Hier

Kopel

Brint

et al. 2020

Preprint

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“…Considering the terminology data could be reused for ontology and knowledge graph construction, we referred to several ontology classi cation or work ow strategy [19,20], and authoritative terminology system UMLS, SNOMED CT for terminology criteria establishment [21,22]. The designed work ow included ve steps, respectively (1) Classi cation schema design (2) Concepts and the sub-concepts assignment (3) Terminology editing strategy (4) Terminology property development (5) Online deployment…”

Section: Methodsmentioning

confidence: 99%

COVID Term: A Bilingual Terminology for COVID-19

Shen

Sun

et al. 2020

Preprint

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Background The coronavirus disease (COVID-19), a pneumonia caused by severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) has shown its destructiveness with more than one million confirmed cases and dozens of thousands of death, which is highly contagious and still spreading globally. World-wide studies have been conducted aiming to understand COVID-19 mechanism, transmission, clinical features, etc. A cross-language terminology of COVID-19 is essential for improving knowledge sharing and scientific discovery dissemination.Methods We developed a bilingual terminology of COVID-19 with mapping Chinese and English terms. The terminology construction follows the workflow (1) Classification schema design; (2) Concepts and sub-concepts assignment; (3) Terminology editing strategy; (4) Terminology property development; (5) Online deployment. We built open access for the terminology named as COVID Term, providing search, browse, and download services.Results The proposed COVID Term include 10 categories: disease, anatomic site, clinical manifestation, demographic and socioeconomic characteristics, living organism, qualifiers, psychological assistance, medical equipment, instruments and materials, epidemic prevention and control, diagnosis and treatment technique respectively. In total, COVID Terms covered 464 concepts with 724 Chinese terms and 887 English terms. All terms are openly accessible online (COVID Term: http://covidterm.imicams.ac.cn ).Conclusions COVID Term is a bilingual terminology focused on COVID-19, the epidemic pneumonia with a high risk of infection around the world. It will provide updated bilingual terms of the disease to help health providers and medical professionals retrieve and exchange information and knowledge in multiple languages. COVID Term was released in machine-readable formats (e.g., XML and JSON), which would contribute to the machine translation and advanced intelligent techniques.

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“…Descendent concepts (children) in the neuro-ontology were not augmented. Ancestor hierarchy was determined by the neuroontology, which is mono-hierarchical [24]. Augmentation vectors were stored in an nxn lookup table (n=1204).…”

Section: Case Abstractionmentioning

confidence: 99%

Evaluation of standard and semantically-augmented distance metrics for neurology patients

Hier

Kopel

Brint

et al. 2020

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Background: Patient distances can be calculated based on signs and symptoms derived from an ontological hierarchy. There is controversy as to whether patient distance metrics that consider the semantic similarity between concepts can outperform standard patient distance metrics that are agnostic to concept similarity. The choice of distance metric can dominate the performance of classification or clustering algorithms. Our objective was to determine if semantically augmented distance metrics would outperform standard metrics on machine learning tasks.Methods: We converted the neurological findings from 382 published neurology cases into sets of concepts with corresponding machine-readable codes. We calculated patient distances by four different metrics (cosine distance, a semantically augmented cosine distance, Jaccard distance, and a semantically augmented bipartite distance). Semantic augmentation for two of the metrics depended on concept similarities from a hierarchical neuro-ontology. For machine learning algorithms, we used the patient diagnosis as the ground truth label and patient findings as machine learning features . We assessed classification accuracy for four classifiers and cluster quality for two clustering algorithms for each of the distance metrics.Results: Inter-patient distances were smaller when the distance metric was semantically augmented. Classification accuracy and cluster quality were not significantly different by distance metric.Conclusion: Although semantic augmentation reduced inter-patient distances, we did not find improved classification accuracy or improved cluster quality with semantically augmented patient distance metrics when applied to a dataset of neurology patients. Further work is needed to assess the utility of semantically augmented patient distances.

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A Neuro-ontology for the neurological examination

Cited by 28 publications

References 29 publications

A patient distance metric for neurology

A patient distance metric for neurology

COVID Term: A Bilingual Terminology for COVID-19

Evaluation of standard and semantically-augmented distance metrics for neurology patients

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