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6 December 2022 Small sample medical event extraction based on data augmentation
Manfu Ma, Xiaoxue Li, Yong Li, Xinyu Zhao, Xia Wang, Hai Jia
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Proceedings Volume 12458, International Conference on Biomedical and Intelligent Systems (IC-BIS 2022); 124583N (2022) https://doi.org/10.1117/12.2660281
Event: International Conference on Biomedical and Intelligent Systems, 2022, Chengdu, China
Abstract
The key problem of event extraction in the medical field is that the cost of medical data labeling is too high, and the labeled samples are scarce, making medical event extraction difficult. In response to this problem, this paper proposes to perform a partial synonymous replacement of training samples to expand the data, and the data and the original data together constitute new electronic medical record data (i.e. EDA data enhancement); In addition, the unlabeled data is predicted by the medical event extraction model to generate labeled data, and then the accurate labeled data is filtered out and added to the original data to form new electronic medical record data, thereby realizing data enhancement (i.e. UDF data enhancement) , to a certain extent, to solve the problem of the scarcity of medical data samples. Based on augmented data, a medical event extraction model (i.e. TEC_MEE model) based on Transformer Encoder and CRF are constructed to extract attributes of specified events from unstructured Chinese electronic medical record text. The experimental results show that, compared with the baseline model, the TEC_MEE model proposed in this paper obtains better medical event extraction results after data enhancement.
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Manfu Ma, Xiaoxue Li, Yong Li, Xinyu Zhao, Xia Wang, and Hai Jia "Small sample medical event extraction based on data augmentation", Proc. SPIE 12458, International Conference on Biomedical and Intelligent Systems (IC-BIS 2022), 124583N (6 December 2022); https://doi.org/10.1117/12.2660281
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KEYWORDS
Data modeling
Tumors
Systems modeling
Performance modeling
Machine learning
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