ArticleJournal of biomedical informatics2011
Towards a framework for developing semantic relatedness reference standards.
Article in Journal of biomedical informatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Usefulness of lung sound data collection using Skeeper SM-300® device: A pilot study.PloS one · 2026Article
- BioLORD-2023: semantic textual representations fusing large language models and clinical knowledge graph insights.Journal of the American Medical Informatics Association : JAMIA · 2024Article
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- Improving medical term embeddings using UMLS Metathesaurus.BMC medical informatics and decision making · 2022Article
- Refining electronic medical records representation in manifold subspace.BMC bioinformatics · 2022Article
- Visualization of medical concepts represented using word embeddings: a scoping review.BMC medical informatics and decision making · 2022Article
- Medical terminology-based computing system: a lightweight post-processing solution for out-of-vocabulary multi-word terms.Frontiers in molecular biosciences · 2022Article
- A Word Pair Dataset for Semantic Similarity and Relatedness in Korean Medical Vocabulary: Reference Development and Validation.JMIR medical informatics · 2021Article
- Lexicon Development for COVID-19-related Concepts Using Open-source Word Embedding Sources: An Intrinsic and Extrinsic Evaluation.JMIR medical informatics · 2021Article
- Comparing general and specialized word embeddings for biomedical named entity recognition.PeerJ. Computer science · 2021Article
- Using word embeddings to improve the privacy of clinical notes.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Exploring semantic deep learning for building reliable and reusable one health knowledge from PubMed systematic reviews and veterinary clinical notes.Journal of biomedical semantics · 2019Article
- Indirect association and ranking hypotheses for literature based discovery.BMC bioinformatics · 2019Article
- Projection Word Embedding Model With Hybrid Sampling Training for Classifying ICD-10-CM Codes: Longitudinal Observational Study.JMIR medical informatics · 2019Article
- Unsupervised low-dimensional vector representations for words, phrases and text that are transparent, scalable, and produce similarity metrics that are not redundant with neural embeddings.Journal of biomedical informatics · 2019Article
- A comparison of word embeddings for the biomedical natural language processing.Journal of biomedical informatics · 2018Article
- Bio-SimVerb and Bio-SimLex: wide-coverage evaluation sets of word similarity in biomedicine.BMC bioinformatics · 2018Article
- Retrofitting Concept Vector Representations of Medical Concepts to Improve Estimates of Semantic Similarity and Relatedness.Studies in health technology and informatics · 2017Article
- tESA: a distributional measure for calculating semantic relatedness.Journal of biomedical semantics · 2016Article
- U-path: An undirected path-based measure of semantic similarity.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2014Article
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Authors and funding
6 authors.
Funding
Abstract
Our objective is to develop a framework for creating reference standards for functional testing of computerized measures of semantic relatedness. Currently, research on computerized approaches to semantic relatedness between biomedical concepts relies on reference standards created for specific purposes using a variety of methods for their analysis. In most cases, these reference standards are not publicly available and the published information provided in manuscripts that evaluate computerized semantic relatedness measurement approaches is not sufficient to reproduce the results. Our proposed framework is based on the experiences of medical informatics and computational linguistics communities and addresses practical and theoretical issues with creating reference standards for semantic relatedness. We demonstrate the use of the framework on a pilot set of 101 medical term pairs rated for semantic relatedness by 13 medical coding experts. While the reliability of this particular reference standard is in the "moderate" range; we show that using clustering and factor analyses offers a data-driven approach to finding systematic differences among raters and identifying groups of potential outliers. We test two ontology-based measures of relatedness and provide both the reference standard containing individual ratings and the R program used to analyze the ratings as open-source. Currently, these resources are intended to be used to reproduce and compare results of studies involving computerized measures of semantic relatedness. Our framework may be extended to the development of reference standards in other research areas in medical informatics including automatic classification, information retrieval from medical records and vocabulary/ontology development.
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