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| Automatic Classification with Unbalanced Data for Electronic Medical Records |
Zhang Yunqiu1( ),Li Bocheng1,Chen Yan2 |
1College of Public Health, Jilin University, Changchun 130021, China
2Shenzhen Health Development Research and Data Management Center, Shenzhen 518028, China |
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Abstract [Objective] This paper proposes an automatic classification method for electronic medical records with unbalanced data, aiming to further improve the classification performance of clinical electronic medical records. [Methods] First, we used the MC-BERT to enhance the semantic representation of electronic medical records. Then, we designed a deep neural network framework to improve the model’s semantic extraction capabilities. Finally, we designed a new loss function from the perspectives of the unbalanced sample categories and difficulty of classification. The proportion of categories, gradient coordination mechanism, and categories similarity were added to the model. [Results] We examined the new model with real electronic medical records. Its accuracy reached 81.37%, while the macro-average F1 value was 65.89%, and the micro-average F1 value was 81.47%. These results are better than the existing methods. [Limitations] We only retrieved medical records from one department. [Conclusions] The proposed method can effectively improve the classification results of unbalanced data.
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Received: 31 August 2021
Published: 14 April 2022
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| Fund:Humanities and Social Science Foundation of Ministry of Education(18YJA870017);Entrusted Project of Shenzhen Medical Information Center(2020（261）);Graduate Innovation Fund of Jilin University(101832020CX279) |
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Corresponding Authors:
Zhang Yunqiu,ORCID：0000-0002-9790-9581
E-mail: yunqiu@jlu.edu.cn
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