AttentiveHerb: A Novel Method for Traditional Medicine Prescription Generation

Liu, Zhi; Zheng, Zeyu; Guo, Xiwang; Qi, Liang; Gui, Jun; Fu, Dianzheng; Yao, Qingfeng; Jin, Luyao

doi:10.1109/access.2019.2941503

Cited by 16 publications

(12 citation statements)

References 31 publications

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“…In addition, its diagnostic methods are unique via look, listen, question and feel the pulse. This is quite different from the modern medicine which mainly rely on medical instruments [1][2][3]. Furthermore, human body is considered as a synthetic system and abundant Chinese traditional naive philosophical thoughts are brought in TCM.…”

Section: Introductionmentioning

confidence: 95%

Mining Syndrome Differentiating Principles from Traditional Chinese Medicine Clinical Data

Ma¹,

Wang²,

Hai³

et al. 2022

Computer Systems Science and Engineering

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Syndrome differentiation-based treatment is one of the key characteristics of Traditional Chinese Medicine (TCM). The process of syndrome differentiation is difficult and challenging due to its complexity, diversity and vagueness. Analyzing syndrome principles from historical records of TCM using data mining (DM) technology has been of high interest in recent years. Nevertheless, in most relevant studies, existing DM algorithms have been simply developed for TCM mining, while the combination of TCM theories or its characteristics with DM algorithms has rarely been reported. This paper presents a novel Symptom-Syndrome Topic Model (SSTM), which is a supervised probabilistic topic model with three-tier Bayesian structure. In the SSTM, syndromes are considered as observed topic labels to distinguish certain symptoms from possible symptoms according to their different positions. The generation of our model is in full compliance with the syndrome differentiation theory of TCM. Experimental results show that the SSTM is more effective than other models for syndrome differentiating.

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Section: Introductionmentioning

confidence: 95%

Mining Syndrome Differentiating Principles from Traditional Chinese Medicine Clinical Data

Ma¹,

Wang²,

Hai³

et al. 2022

Computer Systems Science and Engineering

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“…[15,18,29], which are mainly used in the early stage. During the past decades, deep learning methods own the more significant power in signaling and discovering repurposable drugs, such as RNN [1], GCN [2], CNN [25], GNN [7,28], LSTM [22,30], VAE [4], and Transformer methods [17]. In addition, deep learning approaches can extract more informative features with respect to molecules and mapping these molecule structures to potential spaces.…”

Section: Summary Of Machine Learning Applications In Drug Repurposingmentioning

confidence: 99%

Machine Learning Applications in Drug Repurposing

Yang

Zhang

et al. 2022

Interdiscip Sci Comput Life Sci

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The coronavirus disease (COVID-19) has led to an rush to repurpose existing drugs, although the underlying evidence base is of variable quality. Drug repurposing is a technique by taking advantage of existing known drugs or drug combinations to be explored in an unexpected medical scenario. Drug repurposing, hence, plays a vital role in accelerating the pre-clinical process of designing novel drugs by saving time and cost compared to the traditional de novo drug discovery processes. Since drug repurposing depends on massive observed data from existing drugs and diseases, the tremendous growth of publicly available large-scale machine learning methods supplies the state-of-the-art application of data science to signaling disease, medicine, therapeutics, and identifying targets with the least error. In this article, we introduce guidelines on strategies and options of utilizing machine learning approaches for accelerating drug repurposing. We discuss how to employ machine learning methods in studying precision medicine, and as an instance, how machine learning approaches can accelerate COVID-19 drug repurposing by developing Chinese traditional medicine therapy. This article provides a strong reasonableness for employing machine learning methods for drug repurposing, including during fighting for COVID-19 pandemic.

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“…Attention herb [11], an innovative model of prescription recommendation proposed by Z Liu, can not only learn the relationship between herbs and symptoms, but also can Z Wang et al [18] proposed a new TCM sequence generation architecture named TCM translator, which used seq2seq to explore the relationships between symptoms and herbs, and finally generated prescriptions through LSTM decoding.…”

Section: Deep Learning In Tcmmentioning

confidence: 99%

Chinese Medicine Prescription Recommendation Using Generative Adversarial Network

Rong

Sun

et al. 2022

IEEE Access

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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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AttentiveHerb: A Novel Method for Traditional Medicine Prescription Generation

Cited by 16 publications

References 31 publications

Mining Syndrome Differentiating Principles from Traditional Chinese Medicine Clinical Data

Mining Syndrome Differentiating Principles from Traditional Chinese Medicine Clinical Data

Machine Learning Applications in Drug Repurposing

Chinese Medicine Prescription Recommendation Using Generative Adversarial Network

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