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.
Rule-based approaches rely on heuristics and handcrafted rules to identify entities. They were the dominant approaches in the early CNER systems @cite_30 @cite_12 and some recent work @cite_23 @cite_27 . However, it is difficult to list all rules to model the structure of clinical named entities, and this kind of handcrafted approaches always leads to a relatively high system engineering cost.
@cite_30 @cite_27 @cite_23 @cite_12
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Objective : Development of a general natural-language processor that identifies clinical information in narrative reports and maps that information into a structured representation containing clinical terms. @PARASPLIT Design : The natural-language processor provides three phases of processing, all of which are driven by different knowledge sources. The first phase performs the parsing. It identifies the structure of the text through use of a grammar that defines semantic patterns and a target form. The second phase, regularization, standardizes the terms in the initial target structure via a compositional mapping of multi-word phrases. The third phase, encoding, maps the terms to a controlled vocabulary. Radiology is the test domain for the processor and the target structure is a formal model for representing clinical information in that domain. @PARASPLIT Measurements : The impression sections of 230 radiology reports were encoded by the processor. Results of an automated query of the resultant database for the occurrences of four diseases were compared with the analysis of a panel of three physicians to determine recall and precision. @PARASPLIT Results : Without training specific to the four diseases, recall and precision of the system(combined effect of the processor and query generator) were 70 and 87 . Training of the query component increased recall to 85 without changing precision. We aim to build and evaluate an open-source natural language processing system for information extraction from electronic medical record clinical free-text. We describe and evaluate our system, the clinical Text Analysis and Knowledge Extraction System (cTAKES), released open-source at . The cTAKES builds on existing open-source technologies—the Unstructured Information Management Architecture framework and OpenNLP natural language processing toolkit. Its components, specifically trained for the clinical domain, create rich linguistic and semantic annotations. Performance of individual components: sentence boundary detector accuracy=0.949; tokenizer accuracy=0.949; part-of-speech tagger accuracy=0.936; shallow parser F-score=0.924; named entity recognizer and system-level evaluation F-score=0.715 for exact and 0.824 for overlapping spans, and accuracy for concept mapping, negation, and status attributes for exact and overlapping spans of 0.957, 0.943, 0.859, and 0.580, 0.939, and 0.839, respectively. Overall performance is discussed against five applications. The cTAKES annotations are the foundation for methods and modules for higher-level semantic processing of clinical free-text. Background The text descriptions in electronic medical records are a rich source of information. We have developed a Health Information Text Extraction (HITEx) tool and used it to extract key findings for a research study on airways disease. To solve the mystery of the life phenomenon, we must clarify when genes are expressed and how their products interact with each other. But since the amount of continuously updated knowledge on these interactions is massive and is only available in the form of published articles, an intelligent information extraction (IE) system is needed. To extract these information directly from articles, the system must firstly identify the material names. However, medical and biological documents often include proper nouns newly made by the authors, and conventional methods based on domain specific dictionaries cannot detect such unknown words or coinages. In this study, we propose a new method of extracting material names, PROPER, using surface clue on character strings. It extracts material names in the sentence with 94.70 precision and 98.84 recall, regardless of whether it is already known or newly defined.