Bidirectional RNN for Medical Event Detection in Electronic Health Records
Open accessAbstract
Sequence labeling for extraction of medical events and their attributes from unstructured text in Electronic Health Record (EHR) notes is a key step towards semantic understanding of EHRs. It has important applications in health informatics including pharmacovigilance and drug surveillance. The state of the art supervised machine learning models in this domain are based on Conditional Random Fields (CRFs) with features calculated from fixed context windows. In this application, we explored recurrent neural network frameworks and show that they significantly out-performed the CRF models.
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Deep Learning approaches have gained popularity to overcome challenges faced by traditional NLP approaches in extracting information from EHR.
“To overcome challenges faced by traditional NLP approaches in extracting information from EHR, recently, Deep Learning (DL approaches have gained popularity [45].”
From Secondary Use of Electronic Health Record: Opportunities and Challenges (2020) · cites this paper as [45]
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- Authors
- Jagannatha, A. N., & Yu, H.
- Affiliations
- Edith Nourse Rogers Memorial Veterans Hospital (US)
- Venue
- Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Published
- 2016
- Volume
- 2016
- Pages
- 473-482
- PMC
- PMC5119627
- OpenAlex
- W2469314752
- Cited by
- 314
- References
- 30
- Citations by year
- 2026: 4 · 2025: 15 · 2024: 14 · 2023: 28 · 2022: 28 · 2021: 36 · 2020: 58 · 2019: 68 · 2018: 40 · 2017: 17 · 2016: 6
- FWCI
- 23.53
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- U.S. Department of Veterans Affairs (I01 HX001457)
- National Heart, Lung, and Blood Institute (R01HL125089)
- National Cancer Institute (U01CA180975)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- bidirectional recurrent neural network, electronic health records, sequence labeling, clinical text annotation, conditional random fields, pharmacovigilance, unstructured clinical notes
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