  In this work, an ontology-based model for AI-assisted medicine side-effect
(SE) prediction is developed, where three main components, including the drug
model, the treatment model, and the AI-assisted prediction model, of proposed
model are presented. To validate the proposed model, an ANN structure is
established and trained by two hundred and forty-two TCM prescriptions. These
data are gathered and classified from the most famous ancient TCM book and more
than one thousand SE reports, in which two ontology-based attributions, hot and
cold, are introduced to evaluate whether the prescription will cause SE or not.
The results preliminarily reveal that it is a relationship between the
ontology-based attributions and the corresponding predicted indicator that can
be learnt by AI for predicting the SE, which suggests the proposed model has a
potential in AI-assisted SE prediction. However, it should be noted that, the
proposed model highly depends on the sufficient clinic data, and hereby, much
deeper exploration is important for enhancing the accuracy of the prediction.
