Mining heterogeneous networks with topological features constructed from patient-contributed content for pharmacovigilance

In this study, we propose a framework for drug safety signal detection by harnessing online health community data, a timely, informative, and publicly available data source. Concretely, we used MedHelp as the data source to collect patient-contributed content based on which a weighted heterogeneous network was constructed. We extracted topological features from the network, quantified them with different weighting methods, and used supervised learning method for both ADR and DDI signal detection. In addition, after identifying DDI signals, we proposed a new metric, named Interaction Ratio, to identify associated ADRs due to suspected interactions. The experiment results showed that our proposed techniques outperforms baseline methods.
Source: Artificial Intelligence in Medicine - Category: Bioinformatics Source Type: research