III: Small: Mining Heterogeneous Network Built from Multiple Data Sources to Reduce Opioid Overdose Risks

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As opioid overdose deaths have continued to increase over the past decade across the country, it is critical to understand the drugs involved in those deaths and the potential role of polypharmacy (i.e., the concurrent use of multiple medications) in opioid overdose deaths. However, due to the formidable complexity of drug-drug interactions (DDIs) arising from polypharmacy, it is challenging if not impossible to count them all manually. Therefore, there is an urgent need for developing novel computational methodologies and models for early detection of risky DDI patterns when opioids are combined with other drugs (e.g., sedatives, muscle relaxants, anti-anxieties). Since relying on a single data source for biomedical knowledge discovery often results in unsatisfactory performance, the goal of this project is to design and develop a novel and integrated framework (algorithms, models, and techniques) to construct a heterogeneous network built from multiple data sources and extract useful information from the constructed network to reduce the risk of opioid overdoses resulting from polypharmacy. The key components of the planned research are three-folds. First, the research team will construct a heterogeneous network from multiple data sources for abstract representation. Second, the team will develop scalable techniques for large-scale and dynamic heterogeneous network representation learning. Third, the team will design a novel deep learning framework with interpretability enhancement for early detection of risky DDI patterns when opioids are combined with other medications. The broad impacts of this work include benefits to the general public by addressing one of the most challenging issues facing U.S. public health today (i.e., overdose death prevention). The planned research in this project is also beneficial to the intelligent information management domain where multiple data sources are involved. The project integrates interdisciplinary research with education through curriculum development, the participation of underrepresented groups, and student mentoring activities.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.