ArticleJournal of the American Medical Informatics Association : JAMIA
Applying MetaMap to Medline for identifying novel associations in a large clinical dataset: a feasibility analysis.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- The intersection of COVID-19 and autoimmunity.The Journal of clinical investigation · 2021Review
- Pulse of the pandemic: Iterative topic filtering for clinical information extraction from social media.Journal of biomedical informatics · 2021Article
- A Phenome-Wide Association Study (PheWAS) of COVID-19 Outcomes by Race Using the Electronic Health Records Data in Michigan Medicine.Journal of clinical medicine · 2021Article
- Phenotype risk scores (PheRS) for pancreatic cancer using time-stamped electronic health record data: Discovery and validation in two large biobanks.Journal of biomedical informatics · 2021Article
- Constructing a knowledge-based heterogeneous information graph for medical health status classification.Health information science and systems · 2020Article
- Article
- Mining e-cigarette adverse events in social media using Bi-LSTM recurrent neural network with word embedding representation.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- Improving biomedical information retrieval by linear combinations of different query expansion techniques.BMC bioinformatics · 2016Article
- Interpretable Topic Features for Post-ICU Mortality Prediction.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2016Article
- Identifying Plant-Human Disease Associations in Biomedical Literature: A Case Study.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2016Article
- Article
- Challenges in clinical natural language processing for automated disorder normalization.Journal of biomedical informatics · 2015Article
- Mining and Visualizing Family History Associations in the Electronic Health Record: A Case Study for Pediatric Asthma.AMIA ... Annual Symposium proceedings. AMIA SymposiumArticle
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Authors and funding
7 authors.
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Abstract
objectiveWe describe experiments designed to determine the feasibility of distinguishing known from novel associations based on a clinical dataset comprised of International Classification of Disease, V.9 (ICD-9) codes from 1.6 million patients by comparing them to associations of ICD-9 codes derived from 20.5 million Medline citations processed using MetaMap. Associations appearing only in the clinical dataset, but not in Medline citations, are potentially novel.
methodsPairwise associations of ICD-9 codes were independently identified in both the clinical and Medline datasets, which were then compared to quantify their degree of overlap. We also performed a manual review of a subset of the associations to validate how well MetaMap performed in identifying diagnoses mentioned in Medline citations that formed the basis of the Medline associations.
resultsThe overlap of associations based on ICD-9 codes in the clinical and Medline datasets was low: only 6.6% of the 3.1 million associations found in the clinical dataset were also present in the Medline dataset. Further, a manual review of a subset of the associations that appeared in both datasets revealed that co-occurring diagnoses from Medline citations do not always represent clinically meaningful associations. DISCUSSION: Identifying novel associations derived from large clinical datasets remains challenging. Medline as a sole data source for existing knowledge may not be adequate to filter out widely known associations.
conclusionsIn this study, novel associations were not readily identified. Further improvements in accuracy and relevance for tools such as MetaMap are needed to realize their expected utility.
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