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

2018-09-12 05:04:58
Zeheng Wang, Kun Lu, Jun Cao, Yuanzhe Yao, Liang Li, Runyu Liu, Zhiyuan Liu, Jing Yan

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 that 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 simply introduced to evaluate whether the prediction will cause a SE or not. The results preliminarily reveal that it is a relationship between the ontology-based attributions and the corresponding 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.

Abstract (translated)

在这项工作中,开发了基于本体的AI辅助药物副作用(SE)预测模型,其中提出的模型的三个主要组成部分,包括药物模型,治疗模型和AI辅助预测模型,呈现。为了验证所提出的模型,通过224个TCM处方建立和训练ANN结构,这些处方从最着名的古代TCM书和超过一千个SE报告中收集和分类,其中两个基于本体的归因,简单地介绍冷热,以评估预测是否会导致SE。结果初步揭示,它是基于本体的属性与AI可以用于预测SE的相应指标之间的关系,这表明所提出的模型具有AI辅助SE预测的潜力。然而,应该注意的是,所提出的模型高度依赖于足够的临床数据,因此,更深入的探索对于提高预测的准确性是重要的。

URL

https://arxiv.org/abs/1809.04258

PDF

https://arxiv.org/pdf/1809.04258.pdf


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