  In clinical treatment, identifying potential adverse reactions of drugs can
help assist doctors in making medication decisions. In response to the problems
in previous studies that features are high-dimensional and sparse, independent
prediction models need to be constructed for each adverse reaction of drugs,
and the prediction accuracy is low, this paper develops an adverse drug
reaction prediction model based on knowledge graph embedding and deep learning,
which can predict experimental results. Unified prediction of adverse drug
reactions covered. Knowledge graph embedding technology can fuse the associated
information between drugs and alleviate the shortcomings of high-dimensional
sparsity in feature matrices, and the efficient training capabilities of deep
learning can improve the prediction accuracy of the model. This article builds
an adverse drug reaction knowledge graph based on drug feature data; by
analyzing the embedding effect of the knowledge graph under different embedding
strategies, the best embedding strategy is selected to obtain sample vectors;
and then a convolutional neural network model is constructed to predict adverse
reactions. The results show that under the DistMult embedding model and
400-dimensional embedding strategy, the convolutional neural network model has
the best prediction effect; the average accuracy, F_1 score, recall rate and
area under the curve of repeated experiments are better than the methods
reported in the literature. The obtained prediction model has good prediction
accuracy and stability, and can provide an effective reference for later safe
medication guidance.
