ArticleJournal of the American Medical Informatics Association : JAMIA
A comprehensive study of named entity recognition in Chinese clinical text.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 2 of them syntheses that pooled it.
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Who cites it
45 citing papers in PubMed, 2 syntheses or guidelines pooled it, 191 citations in OpenAlex.
- Current approaches to identify sections within clinical narratives from electronic health records: a systematic review.BMC medical research methodology · 2019Pooled it
- Clinical Natural Language Processing in 2014: Foundational Methods Supporting Efficient Healthcare.Yearbook of medical informatics · 2015Pooled it
- Dynamic few-shot prompting for clinical note section classification using lightweight, open-source large language models.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Identification of Gender Differences in Acute Myocardial Infarction Presentation and Management at Aga Khan University Hospital-Pakistan: Natural Language Processing Application in a Dataset of Patients With Cardiovascular Disease.JMIR formative research · 2024Article
- Nurse training to enhance adherence counselling for HIV-tuberculosis coinfection in South Africa: Integrative review.Curationis · 2024Review
- Evolution of the "Internet Plus Health Care" Mode Enabled by Artificial Intelligence: Development and Application of an Outpatient Triage System.Journal of medical Internet research · 2024Article
- Article
- Enhancing traditional Chinese medical named entity recognition with Dyn-Att Net: a dynamic attention approach.PeerJ. Computer science · 2024Article
- Improving model transferability for clinical note section classification models using continued pretraining.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Named Entity Recognition in Electronic Health Records: A Methodological Review.Healthcare informatics research · 2023Article
- CLART: A cascaded lattice-and-radical transformer network for Chinese medical named entity recognition.Heliyon · 2023Article
- An Alternative Application of Natural Language Processing to Express a Characteristic Feature of Diseases in Japanese Medical Records.Methods of information in medicine · 2023Article
- Improving the Transferability of Clinical Note Section Classification Models with BERT and Large Language Model Ensembles.Proceedings of the conference. Association for Computational Linguistics. Meeting · 2023Article
- A Joint Extraction System Based on Conditional Layer Normalization for Health Monitoring.Sensors (Basel, Switzerland) · 2023Article
- Improving Model Transferability for Clinical Note Section Classification Models Using Continued Pretraining.medRxiv : the preprint server for health sciences · 2023Article
- A BERT-Span model for Chinese named entity recognition in rehabilitation medicine.PeerJ. Computer science · 2023Article
- Named entity recognition of Chinese electronic medical records based on a hybrid neural network and medical MC-BERT.BMC medical informatics and decision making · 2022Article
- Surgical procedure long terms recognition from Chinese literature incorporating structural feature.Heliyon · 2022Article
- Construction of an Assisted Model Based on Natural Language Processing for Automatic Early Diagnosis of Autoimmune Encephalitis.Neurology and therapy · 2022Article
- Research on Named Entity Recognition Method of Metro On-Board Equipment Based on Multiheaded Self-Attention Mechanism and CNN-BiLSTM-CRF.Computational intelligence and neuroscience · 2022Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveNamed entity recognition (NER) is one of the fundamental tasks in natural language processing. In the medical domain, there have been a number of studies on NER in English clinical notes; however, very limited NER research has been carried out on clinical notes written in Chinese. The goal of this study was to systematically investigate features and machine learning algorithms for NER in Chinese clinical text. MATERIALS AND
methodsWe randomly selected 400 admission notes and 400 discharge summaries from Peking Union Medical College Hospital in China. For each note, four types of entity-clinical problems, procedures, laboratory test, and medications-were annotated according to a predefined guideline. Two-thirds of the 400 notes were used to train the NER systems and one-third for testing. We investigated the effects of different types of feature including bag-of-characters, word segmentation, part-of-speech, and section information, and different machine learning algorithms including conditional random fields (CRF), support vector machines (SVM), maximum entropy (ME), and structural SVM (SSVM) on the Chinese clinical NER task. All classifiers were trained on the training dataset and evaluated on the test set, and micro-averaged precision, recall, and F-measure were reported.
resultsOur evaluation on the independent test set showed that most types of feature were beneficial to Chinese NER systems, although the improvements were limited. The system achieved the highest performance by combining word segmentation and section information, indicating that these two types of feature complement each other. When the same types of optimized feature were used, CRF and SSVM outperformed SVM and ME. More specifically, SSVM achieved the highest performance of the four algorithms, with F-measures of 93.51% and 90.01% for admission notes and discharge summaries, respectively.
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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.