ArticleJournal of the American Medical Informatics Association : JAMIA
Hybrid methods for improving information access in clinical documents: concept, assertion, and relation identification.
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 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled 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
- DI++: A deep learning system for patient condition identification in clinical notes.Artificial intelligence in medicine · 2022Article
- Chinese Clinical Named Entity Recognition with ALBERT and MHA Mechanism.Evidence-based complementary and alternative medicine : eCAM · 2022Article
- Clinical Concept Extraction with Lexical Semantics to Support Automatic Annotation.International journal of environmental research and public health · 2021Article
- Differentiating Sense through Semantic Interaction Data.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2016Article
- Methodological Issues in Predicting Pediatric Epilepsy Surgery Candidates Through Natural Language Processing and Machine Learning.Biomedical informatics insights · 2016Article
- Assessing the role of a medication-indication resource in the treatment relation extraction from clinical text.Journal of the American Medical Informatics Association : JAMIA · 2015Article
- "Big data" and the electronic health record.Yearbook of medical informatics · 2014Review
- Learning to identify treatment relations in clinical text.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2014Article
- Automated Assessment of Medical Students' Clinical Exposures according to AAMC Geriatric Competencies.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2014Article
- Electronic health records-driven phenotyping: challenges, recent advances, and perspectives.Journal of the American Medical Informatics Association : JAMIA · 2013Article
- Automated outcome classification of emergency department computed tomography imaging reports.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2013Article
- Combining an expert-based medical entity recognizer to a machine-learning system: methods and a case study.Biomedical informatics insights · 2013Article
- Clinical research informatics: a conceptual perspective.Journal of the American Medical Informatics Association : JAMIA · 2012Article
- Automatic computation of CHA2DS2-VASc score: information extraction from clinical texts for thromboembolism risk assessment.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2011Article
- Feature engineering combined with machine learning and rule-based methods for structured information extraction from narrative clinical discharge summaries.Journal of the American Medical Informatics Association : JAMIAArticle
- Use of a support vector machine for categorizing free-text notes: assessment of accuracy across two institutions.Journal of the American Medical Informatics Association : JAMIAArticle
- Eventual situations for timeline extraction from clinical reports.Journal of the American Medical Informatics Association : JAMIAArticle
- A sequence labeling approach to link medications and their attributes in clinical notes and clinical trial announcements for information extraction.Journal of the American Medical Informatics Association : JAMIAArticle
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
objectiveThis paper describes the approaches the authors developed while participating in the i2b2/VA 2010 challenge to automatically extract medical concepts and annotate assertions on concepts and relations between concepts.
designThe authors'approaches rely on both rule-based and machine-learning methods. Natural language processing is used to extract features from the input texts; these features are then used in the authors' machine-learning approaches. The authors used Conditional Random Fields for concept extraction, and Support Vector Machines for assertion and relation annotation. Depending on the task, the authors tested various combinations of rule-based and machine-learning methods.
resultsThe authors'assertion annotation system obtained an F-measure of 0.931, ranking fifth out of 21 participants at the i2b2/VA 2010 challenge. The authors' relation annotation system ranked third out of 16 participants with a 0.709 F-measure. The 0.773 F-measure the authors obtained on concept extraction did not make it to the top 10.
conclusionOn the one hand, the authors confirm that the use of only machine-learning methods is highly dependent on the annotated training data, and thus obtained better results for well-represented classes. On the other hand, the use of only a rule-based method was not sufficient to deal with new types of data. Finally, the use of hybrid approaches combining machine-learning and rule-based approaches yielded higher scores.
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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.