Abstract Clinical named entity recognition aims to identify and classify clinical terms such as diseases, symptoms, treatments, exams, and body parts in electronic health records, which is a fundamental and crucial task for clinical and translational research. In recent years, deep neural networks have achieved significant success in named entity recognition and many other natural language processing tasks. Most of these algorithms are trained end to end, and can automatically learn features from large scale labeled datasets. However, these data-driven methods typically lack the capability of processing rare or unseen entities. Previous statistical methods and feature engineering practice have demonstrated that human knowledge can provide valuable information for handling rare and unseen cases. In this paper, we propose a new model which combines data-driven deep learning approaches and knowledge-driven dictionary approaches. Specifically, we incorporate dictionaries into deep neural networks. In addition, two different architectures that extend the bi-directional long short-term memory neural network and five different feature representation schemes are also proposed to handle the task. Computational results on the CCKS-2017 Task 2 benchmark dataset show that the proposed method achieves the highly competitive performance compared with the state-of-the-art deep learning methods.
Dictionary-based approaches rely on existing clinical vocabularies to identify entities @cite_21 @cite_14 @cite_25 . They were widely used because of their simplicity and their performance. A dictionary-based CNER system can extract all the matched entities defined in a dictionary from a given clinical text. However, it cannot deal with entities which are not included in the dictionary, and usually causes low recalls.
@cite_14 @cite_21 @cite_25
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The apparatus includes an arm pivoted between its end portions, a foot pedal for operating one of the end portions of the arm, and a switch operated by the other end portion of the arm. The pedal may be the accelerator pedal of an automotive vehicle. When the pedal is depressed, the switch operates immediately, even before the engine of the vehicle speeds up. The preferred switch has a special construction which is also part of the invention. EDGAR (Extraction of Drugs, Genes and Relations) is a natural language processing system that extracts information about drugs and genes relevant to cancer from the biomedical literature. This automatically extracted information has remarkable potential to facilitate computational analysis in the molecular biology of cancer, and the technology is straightforwardly generalizable to many areas of biomedicine. This paper reports on the mechanisms for automatically generating such assertions and on a simple application, conceptual clustering of documents. The system uses a stochastic part of speech tagger, generates an underspecified syntactic parse and then uses semantic and pragmatic information to construct its assertions. The system builds on two important existing resources: the MEDLINE database of biomedical citations and abstracts and the Unified Medical Language System, which provides syntactic and semantic information about the terms found in biomedical abstracts. Background Bio-entity extraction is a pivotal component for information extraction from biomedical literature. The dictionary-based bio-entity extraction is the first generation of Named Entity Recognition (NER) techniques.