Mining a Weighted Heterogeneous Network Extracted from Healthcare-Specific Social Media for Identifying Interactions between Drugs

Drug-drug interaction (DDI) detection is an important issue of pharmacovigilance. Currently, approaches proposed to detection DDIs are mainly focused on data sources such as spontaneous reporting systems, electronic health records, chemical/pharmacological databases, and biomedical literatures. However, those data sources are limited either by low reporting ratio, access issue, or long publication time span. In this work, we propose to explore online health communities, a timely, informative and publicly available data source, for DDI detection. We construct a weighted heterogeneous healthcare network that contains drugs, adverse drug reactions (ADRs), diseases, and users extracted from online health consumer-contributed contents, extract topological features, develop weighted path count to quantify the features, and use supervised learning techniques to detect DDI signals. The experiment results show that weighted heterogeneous healthcare network using leverage and lift are more effective in DDI detection than both unweighted homogeneous and heterogeneous network.