AttentiveHerb: A Novel Method for Traditional Medicine Prescription Generation

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

doi:10.1109/access.2019.2941503

Abstract

In this paper, we propose a novel intelligent model, called AttentiveHerb, for simulating the doctor’s inquiry and prescription that is composed by a series of herbs. It can automatically simulate some principles and learns the interaction between symptoms and herbs from clinical records of traditional herbal medicine. This model consists of two different attention mechanisms for distinguishing the main symptoms and paying different attention to different symptoms. By experiments, in terms of the predicted prescriptions, 51% of the total cases are in full accordance with the labels; in 1.09% of cases, all herbs of a label can be found in the predicted prescription and the predicted prescription have other additional herbs; in 15.4% of cases, all herbs of a predicted prescription can be found in their corresponding label; in 22.41% of cases, several herbs in each predicted prescription overlap with its label; and 10.1% of cases are completely different from the label. In summary, 67.49% of the predicted prescriptions are close to the labels, and 89.9% contain the same herbs with the labels, which indicates that the prescriptions generated by our model are close to those by doctors. Besides, our model can recommend herbs that do not appear in the label prescriptions but are useful for relieving symptoms. It shows that our model can learn some interactions between herbs and symptoms. With enough normalized traditional herbal medical records, this model works more accurately. This study also provides a benchmark for the upcoming researches in intelligent inquiry and prescription generation of traditional herbal medicine.

Highlights

The effective of China’s traditional herbal medicine has been well recognized by more and more people and nations
Our goal is that the designed two attention mechanisms can empower our model the ability of learning the herb-symptom interaction knowledge, the compatibility of herbs and the Traditional Chinese Medicine (TCM) principles such as treatment based on syndrome differentiation, while [18] relieves repetition problem in prescription generation
The main contributions of our work are summarized as follow: (1) We propose a novel method to dynamically model the TCM inquiry and prescription generation processes

Summary

Introduction

The effective of China’s traditional herbal medicine has been well recognized by more and more people and nations. The effect of the herbal medicine on symptom remission have been studied by many researchers around the world [1], [2]. According to the records of the World Health Organization, approximately 4 billion people in the world are using herbal medicine to treat diseases [3], [4]. The Inner Canon of Huang di is the first medical instructive classic with a 2500-year history, which has always been of a significant value in the research of contemporary TCM. There are a lot of contributions to contemporary medicine provided by the researches on traditional herbs. The Artemisia annua Linn is a herb from which Artemisinin is extracted by Youyou Tu and her group

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Methods

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Conclusion