ArticleJournal of the American Medical Informatics Association : JAMIA2014
Joint segmentation and named entity recognition using dual decomposition in Chinese discharge summaries.
Article in Journal of the American Medical Informatics Association : JAMIA, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Named Entity Recognition in Electronic Health Records: A Methodological Review.Healthcare informatics research · 2023Article
- Surgical procedure long terms recognition from Chinese literature incorporating structural feature.Heliyon · 2022Article
- Automatic approach for constructing a knowledge graph of knee osteoarthritis in Chinese.Health information science and systems · 2020Article
- Constructing fine-grained entity recognition corpora based on clinical records of traditional Chinese medicine.BMC medical informatics and decision making · 2020Article
- Clinical Named Entity Recognition from Chinese Electronic Medical Records Based on Deep Learning Pretraining.Journal of healthcare engineering · 2020Article
- Article
- Constructing a Chinese electronic medical record corpus for named entity recognition on resident admit notes.BMC medical informatics and decision making · 2019Article
- A deep learning model incorporating part of speech and self-matching attention for named entity recognition of Chinese electronic medical records.BMC medical informatics and decision making · 2019Article
- Clinical Natural Language Processing in languages other than English: opportunities and challenges.Journal of biomedical semantics · 2018Review
- A cascaded approach for Chinese clinical text de-identification with less annotation effort.Journal of biomedical informatics · 2017Article
- Developing a cardiovascular disease risk factor annotated corpus of Chinese electronic medical records.BMC medical informatics and decision making · 2017Article
- Article
- A Novel Approach towards Medical Entity Recognition in Chinese Clinical Text.Journal of healthcare engineering · 2017Article
- Development and validation of method for defining conditions using Chinese electronic medical record.BMC medical informatics and decision making · 2016Article
- Speculation detection for Chinese clinical notes: Impacts of word segmentation and embedding models.Journal of biomedical informatics · 2016Article
- Bilingual term alignment from comparable corpora in English discharge summary and Chinese discharge summary.BMC bioinformatics · 2015Article
- Named Entity Recognition in Chinese Clinical Text Using Deep Neural Network.Studies in health technology and informatics · 2015Article
- Electronic health records-driven phenotyping: challenges, recent advances, and perspectives.Journal of the American Medical Informatics Association : JAMIA · 2013Article
- A comprehensive study of named entity recognition in Chinese clinical text.Journal of the American Medical Informatics Association : JAMIAArticle
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8 authors.
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Abstract
objectiveIn this paper, we focus on three aspects: (1) to annotate a set of standard corpus in Chinese discharge summaries; (2) to perform word segmentation and named entity recognition in the above corpus; (3) to build a joint model that performs word segmentation and named entity recognition.
designTwo independent systems of word segmentation and named entity recognition were built based on conditional random field models. In the field of natural language processing, while most approaches use a single model to predict outputs, many works have proved that performance of many tasks can be improved by exploiting combined techniques. Therefore, in this paper, we proposed a joint model using dual decomposition to perform both the two tasks in order to exploit correlations between the two tasks. Three sets of features were designed to demonstrate the advantage of the joint model we proposed, compared with independent models, incremental models and a joint model trained on combined labels. MEASUREMENTS: Micro-averaged precision (P), recall (R), and F-measure (F) were used to evaluate results.
resultsThe gold standard corpus is created using 336 Chinese discharge summaries of 71 355 words. The framework using dual decomposition achieved 0.2% improvement for segmentation and 1% improvement for recognition, compared with each of the two tasks alone.
conclusionsThe joint model is efficient and effective in both segmentation and recognition compared with the two individual tasks. The model achieved encouraging results, demonstrating the feasibility of the two tasks.
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