Version 1
: Received: 18 February 2024 / Approved: 19 February 2024 / Online: 19 February 2024 (14:50:47 CET)
How to cite:
ZHAO, H.; Li, Y.; Zhang, S. Symptom Extraction of Internal Medicine Diseases of Traditional Chinese Medicine Based on BERT-BiLSTM-CRF Model. Preprints2024, 2024020957. https://doi.org/10.20944/preprints202402.0957.v1
ZHAO, H.; Li, Y.; Zhang, S. Symptom Extraction of Internal Medicine Diseases of Traditional Chinese Medicine Based on BERT-BiLSTM-CRF Model. Preprints 2024, 2024020957. https://doi.org/10.20944/preprints202402.0957.v1
ZHAO, H.; Li, Y.; Zhang, S. Symptom Extraction of Internal Medicine Diseases of Traditional Chinese Medicine Based on BERT-BiLSTM-CRF Model. Preprints2024, 2024020957. https://doi.org/10.20944/preprints202402.0957.v1
APA Style
ZHAO, H., Li, Y., & Zhang, S. (2024). Symptom Extraction of Internal Medicine Diseases of Traditional Chinese Medicine Based on BERT-BiLSTM-CRF Model. Preprints. https://doi.org/10.20944/preprints202402.0957.v1
Chicago/Turabian Style
ZHAO, H., Yuehan Li and Shuai Zhang. 2024 "Symptom Extraction of Internal Medicine Diseases of Traditional Chinese Medicine Based on BERT-BiLSTM-CRF Model" Preprints. https://doi.org/10.20944/preprints202402.0957.v1
Abstract
This study focuses on the reasoning of symptoms of TCM internal diseases. Taking cough as an example, the BERT-BiLSTM-CRF model is used for entity recognition. Experiments show that the model has the best entity recognition effect on three types of texts, including teaching cases, clinical cases and literature data, and the F1 value is up to 0.967. It can effectively identify symptoms, course of disease, tongue condition and pulse condition in TCM diagnosis and treatment texts, which lays a solid foundation for intelligent assisted syndrome differentiation and treatment of TCM. By constructing four types of entity corpora and using three mainstream entity recognition models for experiments, it is found that the BERT-BiLSTM-CRF model has a good entity recognition effect in three types of data: teaching cases, clinical cases and literature data. Experimental results show that the F1 values of the BERT-bilSTM-CRF model in teaching cases, clinical cases and literature data are 0.967, 0.82 and 0.91, respectively, which provides an effective method for information extraction in the field of TCM syndrome differentiation diagnosis and treatment, and lays a foundation for further research on knowledge reasoning.
Keywords
Named entity recognition; Corpus; Information extraction; BERT-BiLSTM-CRF
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
