ArticleJournal of healthcare engineering2017
A Novel Approach towards Medical Entity Recognition in Chinese Clinical Text.
Article in Journal of healthcare engineering, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Improved XLNet modeling for Chinese named entity recognition of edible fungus.Frontiers in plant science · 2024Article
- Surgical procedure long terms recognition from Chinese literature incorporating structural feature.Heliyon · 2022Article
- Information Extraction from the Text Data on Traditional Chinese Medicine: A Review on Tasks, Challenges, and Methods from 2010 to 2021.Evidence-based complementary and alternative medicine : eCAM · 2022Review
- A Core Drug Discovery Framework from Large-Scale Literature for Cold Pathogenic Disease Treatment in Traditional Chinese Medicine.Journal of healthcare engineering · 2021Article
- Minimalistic Approach to Coreference Resolution in Lithuanian Medical Records.Computational and mathematical methods in medicine · 2019Article
- Clinical Named Entity Recognition From Chinese Electronic Health Records via Machine Learning Methods.JMIR medical informatics · 2018Article
- Unsupervised Medical Entity Recognition and Linking in Chinese Online Medical Text.Journal of healthcare engineering · 2018Article
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Authors and funding
9 authors.
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Abstract
Medical entity recognition, a basic task in the language processing of clinical data, has been extensively studied in analyzing admission notes in alphabetic languages such as English. However, much less work has been done on nonstructural texts that are written in Chinese, or in the setting of differentiation of Chinese drug names between traditional Chinese medicine and Western medicine. Here, we propose a novel cascade-type Chinese medication entity recognition approach that aims at integrating the sentence category classifier from a support vector machine and the conditional random field-based medication entity recognition. We hypothesized that this approach could avoid the side effects of abundant negative samples and improve the performance of the named entity recognition from admission notes written in Chinese. Therefore, we applied this approach to a test set of 324 Chinese-written admission notes with manual annotation by medical experts. Our data demonstrated that this approach had a score of 94.2% in precision, 92.8% in recall, and 93.5% in F-measure for the recognition of traditional Chinese medicine drug names and 91.2% in precision, 92.6% in recall, and 91.7% F-measure for the recognition of Western medicine drug names. The differences in F-measure were significant compared with those in the baseline systems.
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