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Bidirectional RNN for Medical Event Detection in Electronic Health Records.

作者信息

Jagannatha Abhyuday N, Yu Hong

机构信息

University of Massachusetts, MA, USA.

University of Massachusetts, MA, USA; Bedford VAMC and CHOIR, MA, USA.

出版信息

Proc Conf. 2016 Jun;2016:473-482. doi: 10.18653/v1/n16-1056.

DOI:10.18653/v1/n16-1056
PMID:27885364
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5119627/
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.

摘要

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