Evaluation of standard and semantically-augmented distance metrics for neurology patients.
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...
| Publicado en: | BMC Medical Informatics & Decision Making Vol. 20; no. 1 |
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| Autores principales: | , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
BioMed Central
8/26/2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=145300448&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 145300448 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726947 1CI0 jtl: BMC Medical Informatics & Decision Making issn: 14726947 maglogo: N pubinfo: dt: 8/26/2020 vid: 20 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 145300448 145300448 NLM32843023 145300448 10.1186/s12911-020-01217-8 NLM32843023 145300448 ppct: 1 formats: tig: atl: Evaluation of standard and semantically-augmented distance metrics for neurology patients. aug: au: Hier, Daniel B. Kopel, Jonathan Brint, Steven U. Wunsch II, Donald C. Olbricht, Gayla R. Azizi, Sima Allen, Blaine Wunsch, Donald C 2nd affil: Department of Neurology and Rehabilitation, University of Illinois at Chicago, 60612, Chicago, IL, USA sug: subj: Neurology Benchmarking Algorithms Human Cluster Analysis Validation Studies Comparative Studies Evaluation Research Multicenter Studies Ferrans and Powers Quality of Life Index Scales ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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