EAGER: Mining Heterogeneous Network Constructed from Multiple Data Sources

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Relying on a single data source for knowledge discovery often results in unsatisfactory performance because of the missing patterns involving other potential entities and their relationships. This is particularly important in healthcare informatics areas such as pharmacovigilance. Pharmacovigilance is an important healthcare issue due to the impact of the adverse drug reactions. It complicates patients' medical conditions, increase hospital admissions, and contribute to more morbidity and event death. In current pharmacovigilance research, most work only consider a single data source for discovering the associations between the two entities, namely drugs and adverse drug reactions. This project develops a novel framework to integrate multiple data sources, including spontaneous report systems, electronic health records, pharmaceutical databases, scientific literature, and web data, for heterogeneous network mining. Such a heterogeneous network consists of multiple entities, including drugs, adverse drug reactions, patients, diseases, and symptoms, and various types of relationships among such entities.This project extends the capability of machine learning, data analytics, and pharmacovigilance by integrating multiple data sources for pharmacovigilance applications. In particular, the inclusion of patient-centric data on the web creates insights that may not be obtained from traditional data sources mainly contributed by health professionals. The outcomes of the project include techniques for heterogeneous path mining and structural topological pattern mining on four pharmacovigilance applications, namely adverse drug reaction detection, drug-drug interaction, prescribing cascade, and phenotypic information discovery. Such techniques can also be extended for drug repositioning and off-label use identification. The result of this research is beneficial to multiple disciplines including pharmacy, medicine, public health, and computing. The integrated education plan includes incorporating the research findings into courses offered by the Master of Science program in Health Informatics. The outreach plan involves organizing workshops, conferences, and seminars to disseminate the research outcomes.