A dictionary-guided attention network for biomedical named entity recognition in Chinese electronic medical records
Abstract
Biomedical named entity recognition (BNER) is a critical task for biomedical information extraction. Most popular BNER approaches based on deep learning utilize words and characters as features to represent medical texts. However, many medical terminologies are composed of multiple words and characters, and splitting medical terminology into multiple words (or characters) and assigning weight values for each word (or character) by a standard attention mechanism may disperse the attention score and result in a lower weight value for the medical terminology. This paper proposes a Dictionary-guided Attention Network (DGAN) for BNER in Chinese electronic medical records (EMRs). First, the medical concepts are extracted as large-size words to supplement the comprehensive semantic information of the medical terminology by matching the EMR text to the biomedical dictionary. Then, based on the matched dictionary results, an optimized attention strategy is proposed to focus on the medical concept and adaptively assign higher weights to the characters contained in a concept. Furthermore, semisupervised learning is introduced to reduce the manual labeling of data and to handle the entities not defined in the medical dictionary. To validate our new model in recognizing biomedical named entities, we conduct comprehensive experiments on a real-world Chinese EMR dataset and the CCKS2017 dataset. Our promising results illustrate that our method not only achieves a state-of-the-art performance in BNER but also reduces manual data annotation.
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Transformer is valuable for processing and modeling diverse modalities of medical data, encompassing clinical texts, medical images, and time series data.
“In the realm of medical diagnosis, A transformer proves valuable for processing and modeling diverse modalities of medical data, encompassing clinical texts, medical images, and time series data [59,60,61].”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [61]
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- Authors
- Zhu, Z., Li, J., Zhao, Q., & Akhtar, F.
- Affiliations
- Beijing University of Technology (CN)
- Sukkur IBA University (PK)
- Corresponding author
- Zhao, Qing
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2023-11
- Volume
- 231
- Pages
- 120709
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W4380077823
- Cited by
- 24
- References
- 37
- Citations by year
- 2026: 5 · 2025: 5 · 2024: 14
- FWCI
- 2.13
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Expert Systems with Applications (publishedVersion)
- Funders
- National Natural Science Foundation of China (62266041)
- Beijing Municipal Commission of Education (KM202310005031)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- biomedical named entity recognition, Chinese electronic medical records, medical terminology, attention mechanism, semi-supervised learning, biomedical information extraction
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