An Ontology-Based Artificial Intelligence Model for Medicine Side-Effect Prediction: Taking Traditional Chinese Medicine as An Example

TL;DR

This paper presents an ontology-based AI model for predicting side-effects of medicines, specifically Traditional Chinese Medicine, using an ANN trained on prescription data and ontology attributes like hot and cold.

Contribution

It introduces a novel ontology-based framework integrating TCM attributes with AI for side-effect prediction, validated with a new ANN model trained on extensive prescription data.

Findings

Ontology attributes correlate with side-effect predictions

ANN model achieves preliminary prediction capability

Model's accuracy depends on data quality and quantity

Abstract

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.