Chinese medicine prescription recommendation using generative adversarial network
Open accessAbstract
The theory of traditional Chinese medicine (TCM) is an important part of Chinese culture. In the long history, there are a large number of excellent prescriptions, whose laws have been explored by many studies, but few works directly studied the generation of prescriptions. With the rapid development of deep learning, many applications of text generation using neural networks have emerged. Prescriptions are the doctors’ clinical experience and the results of neural networks also come from the accumulated experience. So, it is very feasible to apply deep learning techniques to the recommendation on TCM prescriptions. GAN and its variants have been applied in text generation recently. It has advantages in many aspects, such as the rapid speed of computation, the update of parameters by back propagation without Markov chain and more real data generation with two-players game. We attempted to know the important attributes of prescriptions and use these contents as the training data for variants of GAN to generate prescriptions. Specifically, we attempted to apply SeqGAN (Sequence Generative Adversarial Nets) and CGAN (Conditional Generative Adversarial Nets) to prescription generations. By underlying the knowledge of TCM, the prescriptions with different characteristics can be successfully generated. In the experiments, we conducted the comparative evaluations on the original data with other models. The results showed that applications in the innovation of prescription sequence generations have certain feasibility and significance, even can provide some reference values for the innovation of TCM.
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Deep neural networks, convolutional neural networks, and recurrent neural networks are widely employed for healthcare applications.
“With advanced DL techniques, deep neural networks (DNNs), convolutional neural networks (CNNs), and recurrent neural networks are widely employed for healthcare applications, i.e., epilepsy seizure prediction [52], segmentation of brain tumor [53], Alzheimer detection [54], genomics [55], and medical prescriptions [56].”
From Explainable, trustworthy, and ethical machine learning for healthcare: A survey (2022) · cites this paper as [54]
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- Authors
- Rong, C., Li, X., Sun, X., & Sun, H.
- Affiliations
- Tiangong University (CN)
- Chinese Academy of Civil Aviation Science and Technology (CN)
- Venue
- IEEE Access
- Published
- 2022
- Volume
- 10
- Pages
- 12219-12228
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W4206579782
- Cited by
- 17
- References
- 20
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 6 · 2023: 4 · 2022: 1
- FWCI
- 1.15
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Natural Science Foundation of China (61402329, 61972456)
- Natural Science Foundation of Tianjin (19JCYBJC15400, 21YDTPJC00440)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Health Sciences › Medicine › Complementary and alternative medicine › Traditional Chinese Medicine Studies
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- traditional Chinese medicine, TCM prescriptions, prescription generation, generative adversarial networks, SeqGAN, conditional generative adversarial networks, text generation, drug recommendation
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