A Multi-Agent System for Reporting Suspected Adverse Drug Reactions

Yanqing ji, Fangyang Shen, See-Yan Lau, John Tran


Adverse drug reactions (ADRs) represent a serious worldwide public health problem. Current post-marketing ADR detection approaches largely rely on spontaneous reports filed by various healthcare professionals such as physicians, pharmacists et.al.. Underreporting is a serious deficiency of these methods - the actually reported adverse events represent less than 10% of all cases. Studies show that two important reasons that cause the underreporting are: 1) healthcare professionals are unaware of encountered ADRs, especially for those unusual ADRs; 2) they are too busy to voluntarily report ADRs since it takes a lot of time to fill out the reporting forms. This paper addresses these two issues by developing a multi-agent ADR reporting system. The system 1) helps healthcare professionals detect the potential causal relationship between a drug and an ADR by analyzing patients’ electronic records via both case-based analysis and statistical data mining approach; 2) allows healthcare professionals to add new rules to signal potential ADRs based on their medical expertise; 3) makes the reporting much easier by automatically linking the patients’ electronic data with the reporting form. A functioning prototype of the system has been developed. The proposed data analysis approaches as well as the performance of the system have been tested. The results indicate that this system has a great potential to improve the spontaneous reporting rate of suspected adverse drug reactions.

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DOI: https://doi.org/10.5210/ojphi.v6i3.5579
