ArticleEvidence-based complementary and alternative medicine : eCAM2022
Chinese Clinical Named Entity Recognition with ALBERT and MHA Mechanism.
Article in Evidence-based complementary and alternative medicine : eCAM, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Named Entity Recognition for Chinese Cancer Electronic Health Records-Development and Evaluation of a Domain-Specific BERT Model: Quantitative Study.JMIR medical informatics · 2025Article
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Authors and funding
4 authors.
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Abstract
Traditional clinical named entity recognition methods fail to balance the effectiveness of feature extraction of unstructured text and the complexity of neural network models. We propose a model based on ALBERT and a multihead attention (MHA) mechanism to solve this problem. Structurally, the model first obtains character-level word embeddings through the ALBERT pretraining language model, then inputs the word embeddings into the iterated dilated convolutional neural network model to quickly extract global semantic information, and decodes the predicted labels through conditional random fields to obtain the optimal label sequence. Also, we apply the MHA mechanism to capture intercharacter dependencies from multiple aspects. Furthermore, we use the RAdam optimizer to boost the convergence speed and improve the generalization ability of our model. Experimental results show that our model achieves an F1 score of 85.63% on the CCKS-2019 dataset-an increase of 4.36% compared to the baseline model.
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Registered trials
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.