Predictive and Personalized Drug Query System

Abstract

Several factors need to be carefully considered in using pharmaceutical drugs, such as drug interactions, side effects, and contraindications. To further complicate the matter, the presence of some drug properties, such as side effects, depends on patient characteristics, such as age, gender, and genetic profiles. Our goal is to provide a tool to assist medical professionals and drug consumers in choosing and finding drugs that suit their needs. We develop an approach that allows querying for drugs that satisfy a set of conditions. The approach can tailor the answers based on given patient profiles. Considering the noisiness and incompleteness of publicly available drug data, in contrast to traditional query systems, our approach considers both the answers that exactly match the query and those that closely match the query. We represent drug information as a heterogeneous graph and model answering a query as a subgraph matching problem. To rank answers, our approach leverages the structure and the heterogeneity of the drug graph to quantify the likelihood of edges and score the answers. Our evaluation shows that for quantifying the edge likelihood, our network-based approach can improve the area under receiver operating characteristic by up to 18%, comparing to a baseline approach. We develop a prototype of our system and demonstrate its benefits through several examples.