[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ skip to main content
research-article

Diagnosis knowledge constrained network based on first-order logic for syndrome differentiation

Published: 01 January 2024 Publication History

Abstract

Traditional Chinese medicine (TCM) has been recognized worldwide as a valuable asset of human medicine. The procedure of TCM is to treatment based on syndrome differentiation. However, the effect of TCM syndrome differentiation relies heavily on the experience of doctors. The gratifying progress of machine learning research in recent years has brought new ideas for TCM syndrome differentiation. In this paper, we propose a deep network model for TCM syndrome differentiation, which improves network performance by injecting TCM syndrome differentiation knowledge in the form of first-order logic into the deep network. Experimental results show that the accuracy of our proposed model reaches 89%, which is significantly better than the deep learning model MLP and other traditional machine learning models. In addition, we present the collected and formatted TCM syndrome differentiation (TSD) dataset, which contains more than 40,000 TCM clinical records. Moreover, 45 symptoms (“▪”), 322 patterns(“▪”), and more than 500 symptoms are labeled in TSD respectively. To the best of our knowledge, this is the first TCM syndrome differentiation dataset labeling diseases, syndromes and pattern. Such detailed labeling is helpful to explore the relationship between various elements of syndrome differentiation.

Highlights

Challenges to address the syndrome differentiation issue in TCM.
The TCM diagnostic knowledge is represented by fist-order logic rules.
A TCM dataset includes 40,000 clinical records and 500 labels.

References

[1]
Zhang Q., Zhou J., Zhang B., Computational traditional Chinese medicine diagnosis: A literature survey, Compu Biol Med 133 (2021) 1–18,.
[2]
Xu Q., Guo Q., Wang C., Zhang S., Wen C., Sun T., et al., Network differentiation: A computational method of pathogenesis diagnosis in traditional Chinese medicine based on systems science, Artif Intell Med 118 (2021) 1–11,.
[3]
Chu X., Sun B., Huang Q., Peng S., Zhou Y., Zhang Y., Quantitative knowledge presentation models of traditional Chinese medicine (TCM): A review, Artif Intell Med 103 (2020),.
[4]
Li H., Li X., Li X., Chinese medicine’s syndrome theory, First, China Medical Science Press, Beijing, 2008.
[5]
Song Y., Zhao B., Jia J., Wang X., Xu S., Li Z., et al., A review on different kinds of artificial intelligence solutions in tcm syndrome differentiation application, J Evidence-Based Complementary Altern Med 2021 (2021),.
[6]
Su C., Ren T., Wang G., Yin J., Using K-L divergence based decision tree to build traditional Chinese medicine diagnosis model on COPD, Comput Eng Appl 55 (3) (2019) 225–230,.
[7]
Yan E., Song J., Liu C., Luan J., Hong W., Comparison of support vector machine, back propagation neural network and extreme learning machine for syndrome element differentiation, Artif Intell Rev 53 (2020) 2453–2481,.
[8]
Yan L., Zhou Z., Song Y., Hu Y., Shang H., Zhan L., et al., Study on the identification model of traditional Chinese medicine constitutions based on ML-kNN multi-label learning, World Sci Technol—Modernization Traditional Chin Med Materia Medica 22 (10) (2020) 3558–3562,.
[9]
Li Y., Feature selection and syndrome prediction for rheumatoid arthritis in traditional Chinese medicine, (Master’s thesis) University of Science and Technology of China, 2020,.
[10]
Yao L., Zhang Y., Wei B., Zhang W., Jin Z., A topic modeling approach for traditional Chinese medicine prescriptions, IEEE Trans Knowl Data Eng 30 (6) (2018) 1007–1021,.
[11]
Jin Y, Zhang W, He X, Wang X, Wang X. Syndrome-aware Herb Recommendation with Multi-Graph Convolution Network. In: IEEE international conference on data engineering., Dallas, USA; 2020, p. 145–56.
[12]
Liang Z., Liu J., Ou A., Zhang H., Li Z., Huang X., Deep generative learning for automated EHR diagnosis of traditional Chinese medicine, Comput Meth Programs Biomed 174 (2019) 17–23,.
[13]
Romany F.M., Maha M.A., Amal A.A., Internet of things and synergic deep learning based biomedical tongue color image analysis for disease diagnosis and classification, IEEE Access 9 (2021) 94769–94779,.
[14]
Chen J., Yang D., Cao Y., Ma Y., Wen C., Syndrome differentiation and treatment algorithm model in traditional Chinese medicine based on disease cause, location, characteristics and conditions, IEEE Access 6 (2018) 71801–71813,.
[15]
Chen Z., Zhang X., Qiu R., Sun Y., Zheng R., Pan H., et al., Application of artificial intelligence in tongue diagnosis of traditional Chinese medicine:A review, TMR Mod Herb Med 4 (2021) 52–75,.
[16]
Zhang Q., Bai C., Yang T., Chen Z., Li P., Yu H., A unified smart Chinese medicine framework for healthcare and medical services, IEEE/ACM Trans Comput Biol Bioinform 18 (3) (2021) 882–890,.
[17]
Chen Z., Cao Y., He S., Qiao Y., Development of models for classification of action between heat-clearing herbs and blood-activating stasis-resolving herbs based on theory of traditional Chinese medicine, Chinese Med 13 (2018) 1–11.
[18]
Chen Z., Jiang Y., Zhang X., Zheng R., Qiu R., Sun Y., et al., ResNet18DNN: prediction approach of drug-induced liver injury by deep neural network with ResNet18, Brief Bioinform 23 (2021) 1–9,.
[19]
Chen Z., Jiang Y., Zhang X., Zheng R., Qiu R., Sun Y., et al., The prediction approach of drug-induced liver injury: response to the issues of reproducible science of artificial intelligence in real-world applications, Brief Bioinform 23 (2022) 1–3,.
[20]
Ren M., Huang H., Zhou Y., Cao Q., Bu Y., Gao Y., TCM-SD: A large dataset for syndrome differentiation in traditional Chinese medicine, 2022, arXiv preprint arXiv:2203.10839.
[21]
Zhang X., Chen Z., Gao J., Huang W., Li P., Zhang J., A two-stage deep transfer learning model and its application for medical image processing in traditional Chinese medicine, Knowl-Based Syst 239 (2022),.
[22]
Liang R., Guan B., Chen S., Chen H., Jiang J., Li W., et al., Artificial intelligence meets traditional Chinese medicine: A bridge to opening the magic box of sphygmopalpation for pulse pattern recognition, Digital Chin Med 4 (1) (2021) 1–8,.
[23]
Dai Y., Wang G., Dai J., Geman O., A multimodal deep architecture for traditional Chinese medicine diagnosis, Concurr Comp-Pract E 32 (19) (2020) 1–16,.
[24]
Xu Q., Tang W., Teng F., Peng W., Zhang Y., Li W., et al., Intelligent syndrome differentiation of traditional Chinese medicine by ANN: A case study of chronic obstructive pulmonary disease, IEEE Access 7 (2019) 76167–76175,.
[25]
Ma J., Wang Z., Guo H., Xie Q., Wang T., Chen B., Mining syndrome differentiating principles from traditional Chinese medicine clinical data, Comput Syst Sci Eng 40 (3) (2022) 979–993,.
[26]
Jiang Q., Yang X., Sun X., An aided diagnosis model of sub-health based on rough set and fuzzy mathematics: A case of TCM, J Intell Fuzzy Syst 32 (6) (2017) 4135–4143,.
[27]
Weng H, Liu Z, Maxwell A, Li X, Zhang C, Peng E, et al. Multi-Label Symptom Analysis and Modeling of TCM Diagnosis of Hypertension. In: IEEE international conference on bioinformatics and biomedicine. Madrid, Spain; 2018, p. 1922–9.
[28]
Wen G., Chen H., Li H., Hu Y., Li Y., Wang C., Cross domains adversarial learning for Chinese named entity recognition for online medical consultation, J Biomed Inform 112 (2020),.
[29]
Hu Q., Yu T., Li J., Yu Q., Zhu L., Gu Y., End-to-end syndrome differentiation of yin deficiency and yang deficiency in traditional Chinese medicine, Comput Meth Programs Biomed 174 (2019) 9–15,.
[30]
Liu Z., He H., Yan S., Wang H., Yang T., Li G., End-to-end models to imitate traditional Chinese medicine syndrome differentiation in lung cancer diagnosis: Model development and validation, JMIR Med Inf 8 (6) (2020) 1–11,.
[31]
Xie J., Li Y., Wang N., Xin L., Fang Y., Liu J., Feature selection and syndrome classification for rheumatoid arthritis patients with traditional Chinese medicine treatment, Eur J Integr Med 34 (2020),.
[32]
World Federation of Chinese Medicine Societies J., International standard Chinese-english basic nomenclature of chinese medicine, First, People’s medical publishing house, Beijing, 2008.
[33]
Gan L, Kuang K, Yang Y, Wu F. Judgment Prediction via Injecting Legal Knowledge into Neural Networks. In: Proceedings of AAAI. Virtual Conference; 2021, p. 12866–74.
[34]
Erich P.K., Radko M., Endre P., Triangular norms, First, Springer Science & Business Media, Berlin, 2000.
[35]
Li T, Srikumar V. Augmenting Neural Networks with First-order Logic. In: Proceedings of the 57th annual meeting of the association for computational linguistics. Florence, Italy; 2019, p. 292–302.
[36]
Dong J., A collection of extracts of famous modern TCM medical cases in China, First, People’s Medical Publishing House, Beijing, 2010.

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Artificial Intelligence in Medicine
Artificial Intelligence in Medicine  Volume 147, Issue C
Jan 2024
285 pages

Publisher

Elsevier Science Publishers Ltd.

United Kingdom

Publication History

Published: 01 January 2024

Author Tags

  1. Traditional Chinese medicine
  2. Syndrome differentiation
  3. First-order logic
  4. Deep learning

Qualifiers

  • Research-article

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 0
    Total Downloads
  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 27 Jan 2025

Other Metrics

Citations

View Options

View options

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media