ArticleStudies in health technology and informatics2015
Named Entity Recognition in Chinese Clinical Text Using Deep Neural Network.
Article in Studies in health technology and informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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Who cites it
47 citing papers in PubMed.
- Chinese medical named entity recognition integrating adversarial training and feature enhancement.Scientific reports · 2025Article
- An improved data augmentation approach and its application in medical named entity recognition.BMC medical informatics and decision making · 2024Article
- Model tuning or prompt Tuning? a study of large language models for clinical concept and relation extraction.Journal of biomedical informatics · 2024Article
- A weakly supervised method for named entity recognition of Chinese electronic medical records.Medical & biological engineering & computing · 2023Article
- Contextualized medication information extraction using Transformer-based deep learning architectures.Journal of biomedical informatics · 2023Article
- A prefix and attention map discrimination fusion guided attention for biomedical named entity recognition.BMC bioinformatics · 2023Article
- A large language model for electronic health records.NPJ digital medicine · 2022Article
- Extraction and Quantification of Words Representing Degrees of Diseases: Combining the Fuzzy C-Means Method and Gaussian Membership.JMIR formative research · 2022Article
- A Deep Language Model for Symptom Extraction From Clinical Text and its Application to Extract COVID-19 Symptoms From Social Media.IEEE journal of biomedical and health informatics · 2022Article
- Named Entity Recognition of Medical Text Based on the Deep Neural Network.Journal of healthcare engineering · 2022Article
- Hospital Intelligent Power Operation and Maintenance Information Evaluation with the Long and Short Memory Neural Network.BioMed research international · 2022Article
- Phenonizer: A Fine-Grained Phenotypic Named Entity Recognizer for Chinese Clinical Texts.BioMed research international · 2022Article
- Multi-task learning for Chinese clinical named entity recognition with external knowledge.BMC medical informatics and decision making · 2021Article
- Composition-driven symptom phrase recognition for Chinese medical consultation corpora.BMC medical informatics and decision making · 2021Article
- Using Artificial Intelligence Technology to Solve the Electronic Health Service by Processing the Online Case Information.Journal of healthcare engineering · 2021Article
- Article
- A Novel COVID-19 Data Set and an Effective Deep Learning Approach for the De-Identification of Italian Medical Records.IEEE access : practical innovations, open solutions · 2021Article
- Crosslingual named entity recognition for clinical de-identification applied to a COVID-19 Italian data set.Applied soft computing · 2020Article
- A semi-supervised approach for extracting TCM clinical terms based on feature words.BMC medical informatics and decision making · 2020Article
- Multi-Level Representation Learning for Chinese Medical Entity Recognition: Model Development and Validation.JMIR medical informatics · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Rapid growth in electronic health records (EHRs) use has led to an unprecedented expansion of available clinical data in electronic formats. However, much of the important healthcare information is locked in the narrative documents. Therefore Natural Language Processing (NLP) technologies, e.g., Named Entity Recognition that identifies boundaries and types of entities, has been extensively studied to unlock important clinical information in free text. In this study, we investigated a novel deep learning method to recognize clinical entities in Chinese clinical documents using the minimal feature engineering approach. 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. The experiment results showed that the DNN with word embeddings trained from the large unlabeled corpus outperformed the state-of-the-art CRF's model in the minimal feature engineering setting, achieving the highest F1-score of 0.9280. Further analysis showed that word embeddings derived through unsupervised learning from large unlabeled corpus remarkably improved the DNN with randomized embedding, denoting the usefulness of unsupervised feature learning.
Indexed as
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26262126PMC4624324What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.