Evaluation of standard and semantically-augmented distance metrics for neurology patients
- PDF / 1,640,770 Bytes
- 15 Pages / 595.276 x 790.866 pts Page_size
- 61 Downloads / 154 Views
(2020) 20:203
RESEARCH ARTICLE
Open Access
Evaluation of standard and semanticallyaugmented distance metrics for neurology patients Daniel B. Hier1* , Jonathan Kopel2 , Steven U. Brint1, Donald C. Wunsch II3 , Gayla R. Olbricht4 , Sima Azizi3 and Blaine Allen3
Abstract 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 neuroontology. 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. Keywords: Patient distances, Semantic augmentation, Ontologies, Machine learning, Patient clustering, Patient classification, Distance metrics, neurology
* Correspondence: [email protected] 1 Department of Neurology and Rehabilitation, University of Illinois at Chicago, Chicago, IL 60612, USA Full list of author information is available at the end of the article © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creati
Data Loading...
