Approaching High-Accuracy Side Effect Prediction of Traditional Chinese Medicine Compound Prescription Using Network Embedding and Deep Learning

Zeheng Wang,Yuanzhe Yao,Liang Li,Jing Yan

doi:10.1109/access.2020.2991750

Abstract

In this paper, we realize high-accuracy side-effect prediction of Traditional Chinese Medicine Compound Prescription by introducing network embedding and deep learning. A random walk network that could efficiently interpret the information in the prescription is established from a conventional Bag-of-Word network. After the validation of this random walk network, the highest prediction accuracy reaches 0.908 where a simple five-layer artificial neural network is implemented, rendering this method is promising for Traditional Chinese Medicine side-effect prediction and other medicines with a similar structure such as the compound drugs.

Highlights

It has been proven by clinical experiments that Traditional Chinese Medicine (TCM) could be considered as a supplementary treatment in many diseases, such as cancers and mental disorders [1]–[4]
Some researchers believe that the functionality of TCM compound prescription (CP) comes from certain latent attributions, which could be supported by the traditional Chinese philosophy [7], [8]; others reckon that it is because of the chemical ingredients [9]–[11]
That contradiction suggests that there is a sort of uncertainty in TCM theory, which has been more or less proven by frequent reports of unexpected side effects (SE) after the use of TCM CP [12]

Summary

INTRODUCTION

It has been proven by clinical experiments that Traditional Chinese Medicine (TCM) could be considered as a supplementary treatment in many diseases, such as cancers and mental disorders [1]–[4]. Before the prediction of ANN, we exam the model’s stability and effectivity by the ICC coefficient and Kendall‘s correlation coefficient respectively Taking advantage of these techniques, the critical information, which is the attribution-to-SE relationship, is effectively learned by ANN. A. NETWORKS CONSTRUCTION different TCM CPs are firstly represented by vectors based on the Bag of Word (BOW) model. NETWORKS CONSTRUCTION different TCM CPs are firstly represented by vectors based on the Bag of Word (BOW) model This representation contains the information of the ingredients with dosages as well as their attributions. After the network construction and validation, embedding vectors that represent the information of a prescription and its similar derivatives are gathered to construct the deep learning dataset. The three hidden layers that have more than 60 units are designed to fit the complex relationships between latent attributions, such as Cold and Hot, and their side effects in this TCM CP

RESULT

CONCLUSION