Detecting potential adverse drug reactions from health-related social networks

Abstract

In recent years, adverse drug reactions have drawn more and more attention from the public, which may lead to great damage to the public health and cause massive economic losses to our society. As a result, it becomes a great challenge to detect the potential adverse drug reactions before and after putting drugs into the market. With the development of the Internet, health-related social networks have accumulated large amounts of users’ comments on drugs, which may contribute to detect the adverse drug reactions. To this end, we propose a novel framework to detect potential adverse drug reactions based on healthrelated social networks. In our framework, we first extract mentions of diseases and adverse drug reactions from users’ comments using conditional random fields with different levels of features, and then filter the indications of drugs and known adverse drug reactions by external biomedical resources to obtain the potential adverse drug reactions. On the basis, we propose a modified Skip-gram model to discover associated proteins of potential adverse drug reactions, which will facilitate the biomedical experts to determine the authenticity of the potential adverse reactions. Extensive experiments based on DailyStrength show that our framework is effective for detecting potential adverse drug reactions from users’ comments.