A Personalized Adverse Drug Reaction Early Warning Method Based on Contextual Ontology and Rules Learning
A Personalized Adverse Drug Reaction Early Warning Method Based on Contextual Ontology and Rules Learning作者机构：School of Chinese-German Institute for Applied Engineering Zhejiang University of Science and Technology Hangzhou China School of Statistics and Mathematics Zhongnan University of Economics and Law Wuhan China
出 版 物：《Journal of Software Engineering and Applications》 (软件工程与应用（英文）)
年 卷 期：2023年第16卷第11期
页 面：605-621页
学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）]
主 题：Health Information Services Personalized Contextual Ontology Drug Adverse Reaction Early Warning Reasoning
摘 要：Background: The fatality of adverse drug reactions (ADR) has become one of the major causes of the non-natural disease deaths globally, with the issue of drug safety emerging as a common topic of concern. Objective: The personalized ADR early warning method, based on contextual ontology and rule learning, proposed in this study aims to provide a reference method for personalized health and medical information services. Methods: First, the patient data is formalized, and the user contextual ontology is constructed, reflecting the characteristics of the patient population. The concept of ontology rule learning is then proposed, which is to mine the rules contained in the data set through machine learning to improve the efficiency and scientificity of ontology rule generation. Based on the contextual ontology of ADR, the high-level context information is identified and predicted by means of reasoning, so the occurrence of the specific adverse reaction in patients from different populations is extracted. Results: Finally, using diabetes drugs as an example, contextual information is identified and predicted through reasoning, to mine the occurrence of specific adverse reactions in different patient populations, and realize personalized medication decision-making and early warning of ADR.
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