ArticleJournal of biomedical informatics2012
A hybrid knowledge-based and data-driven approach to identifying semantically similar concepts.
Article in Journal of biomedical informatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Medication-indication knowledge bases: a systematic review and critical appraisal.Journal of the American Medical Informatics Association : JAMIA · 2015Pooled it
- Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review.JMIR medical informatics · 2023Review
- Hierarchical Information Criterion for Variable Abstraction.Proceedings of machine learning research · 2021Article
- Use of the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) for Processing Free Text in Health Care: Systematic Scoping Review.Journal of medical Internet research · 2021Article
- Effect of domain knowledge encoding in CNN model architecture-a prostate cancer study using mpMRI images.PeerJ · 2021Article
- Multi-Ontology Refined Embeddings (MORE): A hybrid multi-ontology and corpus-based semantic representation model for biomedical concepts.Journal of biomedical informatics · 2020Article
- Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review.JMIR medical informatics · 2019Review
- Constructing a biodiversity terminological inventory.PloS one · 2017Article
- Speculation detection for Chinese clinical notes: Impacts of word segmentation and embedding models.Journal of biomedical informatics · 2016Article
- Automated methods for the summarization of electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2015Review
- Survival Analysis with Electronic Health Record Data: Experiments with Chronic Kidney Disease.Statistical analysis and data mining · 2014Article
- A hierarchical knowledge-based approach for retrieving similar medical images described with semantic annotations.Journal of biomedical informatics · 2014Article
- Characterizing the sublanguage of online breast cancer forums for medications, symptoms, and emotions.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2014Article
- U-path: An undirected path-based measure of semantic similarity.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2014Article
- Unsupervised biomedical named entity recognition: experiments with clinical and biological texts.Journal of biomedical informatics · 2013Article
- Lessons learned in replicating data-driven experiments in multiple medical systems and patient populations.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2013Article
- Lessons learned from developing a drug evidence base to support pharmacovigilance.Applied clinical informatics · 2013Article
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Authors and funding
2 authors.
Funding
Abstract
An open research question when leveraging ontological knowledge is when to treat different concepts separately from each other and when to aggregate them. For instance, concepts for the terms "paroxysmal cough" and "nocturnal cough" might be aggregated in a kidney disease study, but should be left separate in a pneumonia study. Determining whether two concepts are similar enough to be aggregated can help build better datasets for data mining purposes and avoid signal dilution. Quantifying the similarity among concepts is a difficult task, however, in part because such similarity is context-dependent. We propose a comprehensive method, which computes a similarity score for a concept pair by combining data-driven and ontology-driven knowledge. We demonstrate our method on concepts from SNOMED-CT and on a corpus of clinical notes of patients with chronic kidney disease. By combining information from usage patterns in clinical notes and from ontological structure, the method can prune out concepts that are simply related from those which are semantically similar. When evaluated against a list of concept pairs annotated for similarity, our method reaches an AUC (area under the curve) of 92%.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.