ArticleJournal of biomedical informatics2015
Mining heart disease risk factors in clinical text with named entity recognition and distributional semantic models.
Article in Journal of biomedical informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
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Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The validity of electronic health data for measuring smoking status: a systematic review and meta-analysis.BMC medical informatics and decision making · 2024Pooled it
- Using Natural Language Processing to Measure and Improve Quality of Diabetes Care: A Systematic Review.Journal of diabetes science and technology · 2021Pooled it
- Clinical concept extraction: A methodology review.Journal of biomedical informatics · 2020Review
- Systematic Evaluation of Research Progress on Natural Language Processing in Medicine Over the Past 20 Years: Bibliometric Study on PubMed.Journal of medical Internet research · 2020Review
- Feature extraction for phenotyping from semantic and knowledge resources.Journal of biomedical informatics · 2019Article
- Combining information from a clinical data warehouse and a pharmaceutical database to generate a framework to detect comorbidities in electronic health records.BMC medical informatics and decision making · 2018Article
- Assessing Information Congruence of Documented Cardiovascular Disease between Electronic Dental and Medical Records.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
- Biomedical Text Categorization Based on Ensemble Pruning and Optimized Topic Modelling.Computational and mathematical methods in medicine · 2018Article
- A new synonym-substitution method to enrich the human phenotype ontology.BMC bioinformatics · 2017Article
- Automatic prediction of coronary artery disease from clinical narratives.Journal of biomedical informatics · 2017Article
- Practical applications for natural language processing in clinical research: The 2014 i2b2/UTHealth shared tasks.Journal of biomedical informatics · 2015Article
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Abstract
We present the design, and analyze the performance of a multi-stage natural language processing system employing named entity recognition, Bayesian statistics, and rule logic to identify and characterize heart disease risk factor events in diabetic patients over time. The system was originally developed for the 2014 i2b2 Challenges in Natural Language in Clinical Data. The system's strengths included a high level of accuracy for identifying named entities associated with heart disease risk factor events. The system's primary weakness was due to inaccuracies when characterizing the attributes of some events. For example, determining the relative time of an event with respect to the record date, whether an event is attributable to the patient's history or the patient's family history, and differentiating between current and prior smoking status. We believe these inaccuracies were due in large part to the lack of an effective approach for integrating context into our event detection model. To address these inaccuracies, we explore the addition of a distributional semantic model for characterizing contextual evidence of heart disease risk factor events. Using this semantic model, we raise our initial 2014 i2b2 Challenges in Natural Language of Clinical data F1 score of 0.838 to 0.890 and increased precision by 10.3% without use of any lexicons that might bias our results.
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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.