ArticleBMC bioinformatics2018
A multitask bi-directional RNN model for named entity recognition on Chinese electronic medical records.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- Optimization and comparison research on adaptive modularization BERT-SPAN model for named entity recognition of SARS-CoV-2 virus proteins.Scientific reports · 2026Article
- Applications of flexible materials in health management assisted by machine learning.RSC advances · 2025Review
- An analysis of FRE @ BC8 SympTEMIST track: named entity recognition.Database : the journal of biological databases and curation · 2024Article
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- Enhancing efficiency and capacity of telehealth services with intelligent triage: a bidirectional LSTM neural network model employing character embedding.BMC medical informatics and decision making · 2023Article
- Hypothesis testing procedure for binary and multi-class FStatistics in medicine · 2023Article
- Development and clinical application of an electronic health record quality control system for pulmonary aspergillosis based on guidelines and natural language processing technology.Journal of thoracic disease · 2022Article
- A Comparative Study of Natural Language Processing Algorithms Based on Cities Changing Diabetes Vulnerability Data.Healthcare (Basel, Switzerland) · 2022Article
- Confidence interval for micro-averagedApplied intelligence (Dordrecht, Netherlands) · 2022Article
- Overview of Algorithms for Natural Language Processing and Time Series Analyses.Acta neurochirurgica. Supplement · 2022Review
- Computational Approaches for Acute Traumatic Brain Injury Image Recognition.Frontiers in neurology · 2022Review
- Construction of a Digestive System Tumor Knowledge Graph Based on Chinese Electronic Medical Records: Development and Usability Study.JMIR medical informatics · 2020Article
- Clinical Named Entity Recognition from Chinese Electronic Medical Records Based on Deep Learning Pretraining.Journal of healthcare engineering · 2020Article
- Article
- The International Conference on Intelligent Biology and Medicine (ICIBM) 2018: bioinformatics towards translational applications.BMC bioinformatics · 2018Article
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7 authors.
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Abstract
backgroundElectronic Medical Record (EMR) comprises patients' medical information gathered by medical stuff for providing better health care. Named Entity Recognition (NER) is a sub-field of information extraction aimed at identifying specific entity terms such as disease, test, symptom, genes etc. NER can be a relief for healthcare providers and medical specialists to extract useful information automatically and avoid unnecessary and unrelated information in EMR. However, limited resources of available EMR pose a great challenge for mining entity terms. Therefore, a multitask bi-directional RNN model is proposed here as a potential solution of data augmentation to enhance NER performance with limited data.
methodsA multitask bi-directional RNN model is proposed for extracting entity terms from Chinese EMR. The proposed model can be divided into a shared layer and a task specific layer. Firstly, vector representation of each word is obtained as a concatenation of word embedding and character embedding. Then Bi-directional RNN is used to extract context information from sentence. After that, all these layers are shared by two different task layers, namely the parts-of-speech tagging task layer and the named entity recognition task layer. These two tasks layers are trained alternatively so that the knowledge learned from named entity recognition task can be enhanced by the knowledge gained from parts-of-speech tagging task.
resultsThe performance of our proposed model has been evaluated in terms of micro average F-score, macro average F-score and accuracy. It is observed that the proposed model outperforms the baseline model in all cases. For instance, experimental results conducted on the discharge summaries show that the micro average F-score and the macro average F-score are improved by 2.41% point and 4.16% point, respectively, and the overall accuracy is improved by 5.66% point.
conclusionsIn this paper, a novel multitask bi-directional RNN model is proposed for improving the performance of named entity recognition in EMR. Evaluation results using real datasets demonstrate the effectiveness of the proposed model.
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