Traditional Chinese medicine diagnostic prediction model for holistic syndrome differentiation based on deep learning
Background: With the development of traditional Chinese medicine (TCM) syndrome knowledge accumulation and artificial intelligence (AI), this study proposes a holistic TCM syndrome differentiation model for the classification prediction of multiple TCM syndromes based on deep learning and accelerate...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-03-01
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| Series: | Integrative Medicine Research |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2213422023000987 |
| _version_ | 1797224286640930816 |
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| author | Zhe Chen Dong Zhang Chunxiang Liu Hui Wang Xinyao Jin Fengwen Yang Junhua Zhang |
| author_facet | Zhe Chen Dong Zhang Chunxiang Liu Hui Wang Xinyao Jin Fengwen Yang Junhua Zhang |
| author_sort | Zhe Chen |
| collection | DOAJ |
| description | Background: With the development of traditional Chinese medicine (TCM) syndrome knowledge accumulation and artificial intelligence (AI), this study proposes a holistic TCM syndrome differentiation model for the classification prediction of multiple TCM syndromes based on deep learning and accelerates the construction of modern foundational TCM equipment. Methods: We searched publicly available TCM guidelines and textbooks for expert knowledge and validated these sources using ten-fold cross-validation. Based on the Bidirectional Encoder Representations from Transformers (BERT) and Convolutional Neural Networks (CNN) models, with the classification constraints from TCM holistic syndrome differentiation, the TCM-BERT-CNN model was constructed, which completes the end-to-end TCM holistic syndrome text classification task through symptom input and syndrome output. We assessed the performance of the model using precision, recall, and F1 scores as evaluation metrics. Results: The TCM-BERT-CNN model had a higher precision (0.926), recall (0.9238), and F1 score (0.9247) than the BERT, TextCNN, Long Short-Term Memory (LSTM) of Recurrent Neural Network (RNN), and attention mechanism-based LSTM (LSTM-attention) models and achieved superior results in model performance and predictive classification of most TCM syndromes. Symptom feature visualization demonstrated that the TCM-BERT-CNN model can effectively identify the correlation and characteristics of symptoms in different syndromes with a strong correlation, which conforms to the diagnostic characteristics of TCM syndromes. Conclusions: The TCM-BERT-CNN model proposed in this study is in accordance with the TCM diagnostic characteristics of holistic syndrome differentiation and can effectively complete diagnostic prediction tasks for various TCM syndromes. The results of this study provide new insights into the development of deep learning models for holistic syndrome differentiation in TCM. |
| first_indexed | 2024-03-08T14:09:17Z |
| format | Article |
| id | doaj.art-b0d40876907c4194be0a3c57c8ad61f8 |
| institution | Directory Open Access Journal |
| issn | 2213-4220 |
| language | English |
| last_indexed | 2024-04-24T13:50:42Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Integrative Medicine Research |
| spelling | doaj.art-b0d40876907c4194be0a3c57c8ad61f82024-04-04T05:04:09ZengElsevierIntegrative Medicine Research2213-42202024-03-01131101019Traditional Chinese medicine diagnostic prediction model for holistic syndrome differentiation based on deep learningZhe Chen0Dong Zhang1Chunxiang Liu2Hui Wang3Xinyao Jin4Fengwen Yang5Junhua Zhang6Evidence-based Medicine Center, Tianjin University of Traditional Chinese Medicine, Tianjin, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China; National Key Laboratory of Chinese Medicine Modernization, Tianjin University of Traditional Chinese Medicine, Tianjin, China; State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, ChinaEvidence-based Medicine Center, Tianjin University of Traditional Chinese Medicine, Tianjin, ChinaEvidence-based Medicine Center, Tianjin University of Traditional Chinese Medicine, Tianjin, ChinaEvidence-based Medicine Center, Tianjin University of Traditional Chinese Medicine, Tianjin, ChinaEvidence-based Medicine Center, Tianjin University of Traditional Chinese Medicine, Tianjin, ChinaEvidence-based Medicine Center, Tianjin University of Traditional Chinese Medicine, Tianjin, China; Corresponding authors at: Evidence-based Medicine Center, Tianjin University of Traditional Chinese Medicine, No. 10 Poyanghu Road, Jinghai District, Tianjin 301617, China.Evidence-based Medicine Center, Tianjin University of Traditional Chinese Medicine, Tianjin, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China; Corresponding authors at: Evidence-based Medicine Center, Tianjin University of Traditional Chinese Medicine, No. 10 Poyanghu Road, Jinghai District, Tianjin 301617, China.Background: With the development of traditional Chinese medicine (TCM) syndrome knowledge accumulation and artificial intelligence (AI), this study proposes a holistic TCM syndrome differentiation model for the classification prediction of multiple TCM syndromes based on deep learning and accelerates the construction of modern foundational TCM equipment. Methods: We searched publicly available TCM guidelines and textbooks for expert knowledge and validated these sources using ten-fold cross-validation. Based on the Bidirectional Encoder Representations from Transformers (BERT) and Convolutional Neural Networks (CNN) models, with the classification constraints from TCM holistic syndrome differentiation, the TCM-BERT-CNN model was constructed, which completes the end-to-end TCM holistic syndrome text classification task through symptom input and syndrome output. We assessed the performance of the model using precision, recall, and F1 scores as evaluation metrics. Results: The TCM-BERT-CNN model had a higher precision (0.926), recall (0.9238), and F1 score (0.9247) than the BERT, TextCNN, Long Short-Term Memory (LSTM) of Recurrent Neural Network (RNN), and attention mechanism-based LSTM (LSTM-attention) models and achieved superior results in model performance and predictive classification of most TCM syndromes. Symptom feature visualization demonstrated that the TCM-BERT-CNN model can effectively identify the correlation and characteristics of symptoms in different syndromes with a strong correlation, which conforms to the diagnostic characteristics of TCM syndromes. Conclusions: The TCM-BERT-CNN model proposed in this study is in accordance with the TCM diagnostic characteristics of holistic syndrome differentiation and can effectively complete diagnostic prediction tasks for various TCM syndromes. The results of this study provide new insights into the development of deep learning models for holistic syndrome differentiation in TCM.http://www.sciencedirect.com/science/article/pii/S2213422023000987Traditional Chinese medicine syndromesDeep learningHolistic syndrome differentiationTCM-BERT-CNN modelArtificial intelligence |
| spellingShingle | Zhe Chen Dong Zhang Chunxiang Liu Hui Wang Xinyao Jin Fengwen Yang Junhua Zhang Traditional Chinese medicine diagnostic prediction model for holistic syndrome differentiation based on deep learning Integrative Medicine Research Traditional Chinese medicine syndromes Deep learning Holistic syndrome differentiation TCM-BERT-CNN model Artificial intelligence |
| title | Traditional Chinese medicine diagnostic prediction model for holistic syndrome differentiation based on deep learning |
| title_full | Traditional Chinese medicine diagnostic prediction model for holistic syndrome differentiation based on deep learning |
| title_fullStr | Traditional Chinese medicine diagnostic prediction model for holistic syndrome differentiation based on deep learning |
| title_full_unstemmed | Traditional Chinese medicine diagnostic prediction model for holistic syndrome differentiation based on deep learning |
| title_short | Traditional Chinese medicine diagnostic prediction model for holistic syndrome differentiation based on deep learning |
| title_sort | traditional chinese medicine diagnostic prediction model for holistic syndrome differentiation based on deep learning |
| topic | Traditional Chinese medicine syndromes Deep learning Holistic syndrome differentiation TCM-BERT-CNN model Artificial intelligence |
| url | http://www.sciencedirect.com/science/article/pii/S2213422023000987 |
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