Discovering Drug-Drug Interactions and Associated Adverse Drug Reactions with Triad Prediction in Heterogeneous Healthcare Networks

Drug-drug interactions (DDIs) are of great importance in drug safety. Currently, DDI signal detection mainly depends on post-marketing surveillance. Various data sources have been used by researchers for DDI detection such as spontaneous reporting system, electronic health records, Pharmacological Databases, and biomedical literatures. However, these data sources are limited by either high underreporting ratio, access difficulty, or long publication cycle. In this work, we propose to utilize consumer-contributedcontents from online health communities, a publicly available, mountainous, and timely data source, for identifying DDI signals and association adverse drug reactions (ADRs). Specifically, we first construct a heterogeneous healthcare network, extract different topological types of Drug-Drug-ADR triad, explore node-based, link-based, and triad-based features, and then formulate the signal detection as a supervised learning problem. The experiment results show that our proposed techniques are effective in detecting DDI signals and associated ADRs at the same time.