ArticleJournal of biomedical informatics2018
Extracting similar terms from multiple EMR-based semantic embeddings to support chart reviews.
Article in Journal of biomedical informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Clinical Information Retrieval: A Literature Review.Journal of healthcare informatics research · 2024Article
- Leveraging medical context to recommend semantically similar terms for chart reviews.BMC medical informatics and decision making · 2021Article
- Collecting specialty-related medical terms: Development and evaluation of a resource for Spanish.BMC medical informatics and decision making · 2021Article
- Use, Impact, Weaknesses, and Advanced Features of Search Functions for Clinical Use in Electronic Health Records: A Scoping Review.Applied clinical informatics · 2021Article
- Disease Concept-Embedding Based on the Self-Supervised Method for Medical Information Extraction from Electronic Health Records and Disease Retrieval: Algorithm Development and Validation Study.Journal of medical Internet research · 2021Article
- Development of a Lexicon for Pain.Frontiers in digital health · 2021Article
- Intrinsic Evaluation of Contextual and Non-contextual Word Embeddings using Radiology Reports.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2021Article
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2 authors.
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Abstract
objectiveWord embeddings project semantically similar terms into nearby points in a vector space. When trained on clinical text, these embeddings can be leveraged to improve keyword search and text highlighting. In this paper, we present methods to refine the selection process of similar terms from multiple EMR-based word embeddings, and evaluate their performance quantitatively and qualitatively across multiple chart review tasks. MATERIALS AND
methodsWord embeddings were trained on each clinical note type in an EMR. These embeddings were then combined, weighted, and truncated to select a refined set of similar terms to be used in keyword search and text highlighting. To evaluate their quality, we measured the similar terms' information retrieval (IR) performance using precision-at-K (P@5, P@10). Additionally a user study evaluated users' search term preferences, while a timing study measured the time to answer a question from a clinical chart.
resultsThe refined terms outperformed the baseline method's information retrieval performance (e.g., increasing the average P@5 from 0.48 to 0.60). Additionally, the refined terms were preferred by most users, and reduced the average time to answer a question.
conclusionsClinical information can be more quickly retrieved and synthesized when using semantically similar term from multiple embeddings.
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Registered trials
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