Bidirectional RNN for Medical Event Detection in Electronic Health Records

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 stat...

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Published inProceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting Vol. 2016; p. 473
Main Authors Jagannatha, Abhyuday N, Yu, Hong
Format Journal Article
LanguageEnglish
Published United States 01.06.2016
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Abstract 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.
AbstractList 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.
Author Yu, Hong
Jagannatha, Abhyuday N
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Snippet Sequence labeling for extraction of medical events and their attributes from unstructured text in Electronic Health Record (EHR) notes is a key step towards...
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Title Bidirectional RNN for Medical Event Detection in Electronic Health Records
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