A study on the solution of the problem of extracting essential substance of TCM diagnosis and treatment of hypertension based on triple extraction strategy

Corresponding author: SunYue, sunyue@nhei.cn

DOI: 10.12201/bmr.202107.00015

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  Abstract: Objective To propose a new entity extraction model based on triplet information extraction strategy to solve the problem of entity dispersion when traditional named entity recognition method is applied to TCM medical entity recognition, and to provide methodological reference for automatic extraction of TCM medical entity. Methods Pre-trained BERT for TCM domain were established in this paper. And then, 2000 cases of TCM hypertension were selected to train and test several relational extraction models: conventional BiGRU model; The joint extraction model based on BERT_TCM; CASREL model based on BERT_TCM and FGM/PGD adversarial training. Results Precision, recall and F1-score were used as evaluation metrics. And CASREL model based on BERT_TCM and PGD work best, with a result of precision 0.8887, recall 0.8841 and F1-score 0.8932. Conclusion Based on triple extraction strategy of hypertension medical entity extraction model can effectively solve the problem; BERT_TCM for TCM specific scene has better performance in TCM hypertension relationship extraction task; The joint extraction model significantly improves the performance of the model compared with BiGRU model. CASREL model has better performance than joint extraction model. Introduction of confrontation training technology can effectively improve model robustness.

  Key words: Joint extraction、Casrel extraction、Named entity recognition、hypertension、Cases of traditional Chinese Medicine

  Submit time: 26 August 2021

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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