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

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.