Clinical Named Entity Recognition for identifying sensitive information in clinical text, also known as Clinical De-identification, has long been critical task in medical intelligence. It aims at identifying various types of protected health information (PHI) from clinical text and then replace them ...
Clinical Named Entity Recognition (CNER) is a critical task which aims to identify and classify clinical terms in electronic medical records. In recent years, deep neural networks have achieved significant success in CNER. However, these methods require high-quality and large-scale labeled clinical ...
Guohai Xu, Chengyu Wang, and Xiaofeng He. Improving clinical named entity recognition with global neural attention. In Yi Cai, Yoshiharu Ishikawa, and Jianliang Xu, editors, Web and Big Data, pages 264-279, Cham, 2018. Springer International Publishing....
Clinical named entity recognition (CNER) is a fundamental step for many clinical Natural Language Processing (NLP) systems, which aims to recognize and classify clinical entities such as diseases, symptoms, exams, body parts and treatments in clinical free texts. In recent years, with the developme...
CliNER is implemented as a two-pass machine learning system for named entity recognition, currently using a Conditional Random Fields (CRF) classifier to establish concept boundaries and a Support Vector Machine (SVM) classifier to establish the type of concept. ...
Named entity recognition (NER) on Chinese electronic medical/healthcare records has attracted significantly attentions as it can be applied to building applications to understand these records. Most previous methods have been purely data-driven, requirin
Clinical named entity recognition Convolutional neural network Attention mechanism Residual structure 1. Introduction Named entity recognition (NER) is a fundamental and critical task for other natural language processing (NLP) tasks like relation extraction. With the explosive growth of medical data, clin...
Code for paperChinese clinical named entity recognition with variant neural structures based on BERT methods Paper url:https://www.sciencedirect.com/science/article/pii/S1532046420300502 We pre-trained BERT model to improve the performance of Chinese CNER. Different layers such as Long Short-Term Mem...
Clinical Named Entity Recognition (NER) is a critical task for extracting important patient information from clinical text to support clinical and translational research. This study explored the neural word embeddings derived from a large unlabeled clinical corpus for clinical NER. We systematica...
We developed a deep neural network (DNN) to generate word embeddings from a large unlabeled corpus through unsupervised learning and another DNN for the NER task. 两次DNN方法,一次生成词嵌入,第二次用作实体识别。 Introduction 介绍electronic health record的应用价值,以及面临实体识别的问题。