Chinese Clinical Named Entity Recognition(CNER)is a crucial step in extracting medical information and is of great significance in promoting medical informatization.However,CNER poses challenges due to the specificity of clinical terminology,the complexity of Chinese text semantics,and the uncertainty of ...
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...
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...
Clinical named entities segmentation and recognition are two related tasks and their outputs potentially have mutual benefits for each other as well. Specifically, the output of NES could reduce the searching space of NER and vice versa. Therefore, We present a multi-task learning framework to trai...
Chinese clinical named entity recognition using residual dilated convolutional neural network with conditional random field. IEEE Trans Nanobiosci. 2019;18(3):306–15. Article Google Scholar Li N, Luo L, Ding Z, et al. DUTIR at the CCKS-2019 Task1: Improving Chinese clinical named entity ...
Introduction 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 Sho...
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 achieve...
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 systemati...
Leveraging Multi-source knowledge for Chinese clinical named entity recognition via relational graph convolutional network. J Biomed Inform. 2022;128:104035. Article PubMed Google Scholar Fries JA, Steinberg E, Khattar S, et al. Ontology-driven weak supervision for clinical entity classification in...
CCKS2019 Chinese Clinical NER The word2vec BiLSTM-CRF model for CCKS2019 Chinese clinical named entity recognition. Dependencies python 3.6 gensim 3.4.0 jieba 0.39 keras 2.2.4 keras_contrib 2.0.8 numpy 1.16.4 pandas 0.24.2 Dataset The dataset is provided by the CCKS2019. 文本疾病和诊断影像检...