Drug-Drug Interactions Detection from Online Heterogeneous Healthcare Networks

Abstract

Drug-drug interactions (DDIs) are a serious drug safety problem for health consumers and how to detect such interactions effectively and efficiently has been of great medical significance. Currently, methods proposed to detect DDIs are mainly based on data sources such as clinical trial data, spontaneous reporting systems, electronic medical records, and chemical/pharmacological databases. However, those data sources are limited either by cohort biases, low reporting ratio, or access issue. In this study, we propose to use online healthcare social media, an informative and publicly available data source, to detect DDI signals. We construct a heterogeneous healthcare network based on consumer contributed contents, develop heterogeneous topological features, and use logistic regression as prediction model for DDI detection. The experiment results show that the proposed heterogeneous topological features substantially outperform the homogenous ones in the training set but only slightly outperform the homogeneous ones in the testing set, and interesting heterogeneous paths with strong predictive power are discovered.