Kernelized Multitask Learning Method for Personalized Signaling Adverse Drug Reactions

The signaling of the associations between drugs and adverse drug reactions (ADRs) is a challenging task in pharmacovigilance, especially when an association is infrequent or has never previously been reported. Most existing methods for ADR signaling are based on analyzing the frequency with which drugs tend to co-occur with ADRs. In this article, we propose a kernelized multitask learning model, $\mathtt {\mathtt {KEMULA}}$KEMULA, in which information is learned and transferred from the clinical data of other patients as collaborative information to rank distinct lists of ADRs for different patients. We comprehensively compare the performance of $\mathtt {\mathtt {KEMULA}}$KEMULA against three baseline methods, two state-of-the-art ADR signaling methods, and two $\mathtt {\mathtt {KEMULA}}$KEMULA variants. The method is tested on adverse drug event reports retrieved from the FDA Adverse Event Reporting System (FAERS), which includes 4,106,633 unique adverse drug event reports, 7,824 unique ADRs, 114 unique biotech drugs, 1,151 unique small molecule drugs, and 3,363 unique medical conditions. The experimental results demonstrate the advantages of our method and show that it not only can signal frequent ADRs but also has the power to signal infrequent ADRs that cannot be signaled by most existing methods.