TCM disease diagnosis based on convolutional cyclic neural network algorithm

Authors

  • Fanpeng Kong
  • Mingchuan Zhang

DOI:

https://doi.org/10.54097/jceim.v10i3.8706

Keywords:

TCM diagnosis of diseases, Recurrent Neural Network, Convolutional Neural Network

Abstract

The unique diagnosis and treatment mode of traditional Chinese medicine provides a lot of diagnostic basis with reference value for modern medicine, which is welcomed by people all over the world and has attracted extensive attention of medical researchers. However, the unique diagnosis and treatment method of traditional Chinese medicine also brings difficulties to the dissemination of traditional Chinese medicine. The diagnosis method of traditional Chinese medicine is difficult to objectively and quantitatively express, and the diagnosis process is also closely related to the subjective experience of traditional Chinese medicine doctors. , lacking established standards. In view of the above-mentioned difficulties in the modernization of traditional Chinese medicine, the development of modern information science and technology has brought a turning point to the intelligentization of disease differentiation in traditional Chinese medicine. The core of traditional Chinese medicine diagnosis and treatment is diagnosis and classification. How to convert the difficult to describe disease diagnosis and treatment process of traditional Chinese medicine into language that can be distinguished and understood by ordinary doctors, and build an auxiliary diagnosis and treatment model to provide reference for Chinese doctors to see a doctor is the main problem to be solved in this paper.

References

M. Chen, W.-Z. Li, L. Qian, et al. Ming chen 2020 next poi recommendation based on location interest mining with recurrent neural networks. Journal of Computer Science and Technology (English version), vol. 35, no. 3, pp. 603–616, 5 2020.

Y. Yu, X. Si, C. Hu, et al. A review of recurrent neural networks: Lstm cells and network architectures. Neural computation, vol. 31, no. 7, pp. 1235–1270, 2019.

C. P. Khatri, N. Parikh, S. N. Solanki, et al. Snippet extractor: Recurrent neural networks for text summarization at industry scale. 2020.

B. Li, Y. He.A feature-extraction-based lightweight convolutional and recurrent neural networks adaptive computing model for container terminal liner handling volume forecasting. Discrete Dynamics in Nature and Society, vol. 2021, no. 1, pp. 1–17, 2021.

S. E. Okel, F. Sommen, E. Selmanaj, et al. Tissue-border detection in volumetric laser endomicroscopy using bi-directional gated recurrent neural networks. 2021.

N. Padmaja, P. Balasubramaniam. Finite-time passification offractional-order recurrent neural networks withproportional delay andimpulses: anlmi approach. 2021.

S. Jain, C. Bruckmann, C. Mcdougall. Nft appraisal prediction: Utilizing search trends, public market data,linear regression and recurrent neural networks. 2022.

F. M. Awan, R. Minerva, N. Crespi.Using noise pollution data for traffic prediction in smart cities: Experiments based on lstm recurrent neural networks. IEEE Sensors Journal, vol. PP, no. 99, pp. 1–1, 2021.

Y. Li, X. Wang. Almost periodic solutions in distribution of clifford-valued stochastic recurrent neural networks with time-varying delays. Chaos, Solitons Fractals, vol. 153, 2021.

G. J. Pan, Y. P. Liu. Research and discussion on academic thoughts of diagnosis and treatment of phlegm disease in tcm during the ming dynasty. China Journal of Traditional Chinese Medicine and Pharmacy, pp. 494– 497, 2009.

C. J. Zhang, Z. S. Yan, L. Q. Song. An algorithm of mining association rules of treatment information dealing with tcm nephrosis. Journal of Natural Science of Heilongjiang University, 2005.

J. P. Yang, H. Zhao, Y. Z. Du, et al. Study on quantitative diagnosis model of tcm syndromes of post-stroke depression based on combination of disease and syndrome. Medicine, vol. 100, no. 12, p. e25041, 2021.

S. Adavanne, P. Pertil, T. Virtanen. Sound event detection using spatial features and convolutional recurrent neural network. IEEE, 2017.

C. C. Kao, W. Wang, M. Sun, et al. R-crnn: Region-based convolutional recurrent neural network for audio event detection. Interspeech 2018, 2018.

N. K. Kim, K. K. Hong. Polyphonic sound event detection based on residual convolutional recurrent neural network with semi-supervised loss function. IEEE Access, vol. PP, no. 99, pp. 1–1, 2021.

A. Onan. Bidirectional convolutional recurrent neural network architecture with group-wise enhancement mechanism for text sentiment classification. 2022.

K. Kobayashi, S. Matsushita, N. Shimizu, et al. Automated detection of mouse scratching behaviour using convolutional recurrent neural network. Scientific Reports, vol. 11, no. 1, p. 658, 2021.

J. Yi, J. Park. Hypergraph convolutional recurrent neural network. 2020.

Z. Cui, K. Henrickson, R. Ke, et al. Traffic graph convolutional recurrent neural network: A deep learning framework for network-scale traffic learning and forecasting. IEEE Transactions on

Downloads

Published

24-05-2023

Issue

Section

Articles

How to Cite

Kong, F., & Zhang, M. (2023). TCM disease diagnosis based on convolutional cyclic neural network algorithm. Journal of Computing and Electronic Information Management, 10(3), 80-85. https://doi.org/10.54097/jceim.v10i3.8706

Similar Articles

1-10 of 70

You may also start an advanced similarity search for this article.