ArticleJournal of biomedical informatics2015
Comparison of UMLS terminologies to identify risk of heart disease using clinical notes.
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 13 papers, 3 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Using Natural Language Processing to Measure and Improve Quality of Diabetes Care: A Systematic Review.Journal of diabetes science and technology · 2021Pooled it
- Natural language processing algorithms for mapping clinical text fragments onto ontology concepts: a systematic review and recommendations for future studies.Journal of biomedical semantics · 2020Pooled it
- Current approaches to identify sections within clinical narratives from electronic health records: a systematic review.BMC medical research methodology · 2019Pooled it
- StrokeClassifier: ischemic stroke etiology classification by ensemble consensus modeling using electronic health records.NPJ digital medicine · 2024Article
- Article
- Heart disease risk factors detection from electronic health records using advanced NLP and deep learning techniques.Scientific reports · 2023Article
- The Unified Medical Language System at 30 Years and How It Is Used and Published: Systematic Review and Content Analysis.JMIR medical informatics · 2021Review
- Clinical Note Section Detection Using a Hidden Markov Model of Unified Medical Language System Semantic Types.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2021Article
- Development of an automated phenotyping algorithm for hepatorenal syndrome.Journal of biomedical informatics · 2018Article
- Automatic prediction of coronary artery disease from clinical narratives.Journal of biomedical informatics · 2017Article
- Identifying risk factors for heart disease over time: Overview of 2014 i2b2/UTHealth shared task Track 2.Journal of biomedical informatics · 2015Review
- Practical applications for natural language processing in clinical research: The 2014 i2b2/UTHealth shared tasks.Journal of biomedical informatics · 2015Article
- Textual inference for eligibility criteria resolution in clinical trials.Journal of biomedical informatics · 2015Article
Corrections and comments
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Authors and funding
4 authors.
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Abstract
The second track of the 2014 i2b2 challenge asked participants to automatically identify risk factors for heart disease among diabetic patients using natural language processing techniques for clinical notes. This paper describes a rule-based system developed using a combination of regular expressions, concepts from the Unified Medical Language System (UMLS), and freely-available resources from the community. With a performance (F1=90.7) that is significantly higher than the median (F1=87.20) and close to the top performing system (F1=92.8), it was the best rule-based system of all the submissions in the challenge. We also used this system to evaluate the utility of different terminologies in the UMLS towards the challenge task. Of the 155 terminologies in the UMLS, 129 (76.78%) have no representation in the corpus. The Consumer Health Vocabulary had very good coverage of relevant concepts and was the most useful terminology for the challenge task. While segmenting notes into sections and lists has a significant impact on the performance, identifying negations and experiencer of the medical event results in negligible gain.
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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.