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

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 the proposed model are presented. To validate the proposed model, an ANN structure is established and trained by two hundred 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.

Evaluating the Traditional Chinese Medicine (TCM) Officially Recommended in China for COVID-19 Using Ontology-Based Side-Effect Prediction Framework (OSPF) and Deep Learning

“…By considering the dosage of each component, one compound prescription could be digitalized as a group of data each item inside containing the name of the herb, category name of this herb (hot or cold), and the dosage of the herb. This dataset could be associated with labels that represent if the drug causes the non-negligible SE according to the clinic reports (Yao et al, 2019). Meanwhile, a group of TCM prescriptions coming from the ancient prestigious TCM book Shanghanzebinglun are introduced as the standard prescriptions that are relatively safe.…”

“…Meanwhile, a group of TCM prescriptions coming from the ancient prestigious TCM book Shanghanzebinglun are introduced as the standard prescriptions that are relatively safe. More information of the OSPF and the database could be found in our previous work (Yao et al, 2019). Fig.…”

“…And, as one emerging technology of AI, the Artificial Neural Network (ANN) is getting more and more mature as well in describing the relationships between each part of the paired data (Zhang et al, 2020). Furthermore, this technology has already successfully provided a suitable approach towards high-accuracy TCM SE prediction based on an ontology-based prediction model, suggesting it is of great worth to adopt this method in predicting TCM SE occurrence possibility (Yao et al, 2019). In addition, research works that predict potential available modern antiviral drugs for COVID-19 using deep learning have been done recently (Beck et al, 2020).…”

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

“…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]. In radical cases, the application of TCM in the same disease may cause opposite results [13]- [17].…”

“…In this work, a Node2vec-based SE prediction strategy of TCM CP is proposed and investigated. A model developed in our previous work is used to classify the key attribution of each herb in the TCM CP into cold and hot [12]. Before the prediction of ANN, we also exam the model's stability and effectivity by the ICC coefficient and Kendall's correlation coefficient respectively.…”