Data Integration Core

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ABSTRACT Obstructive sleep apnea (OSA) is a common chronic disorder affecting over 1 billion people worldwide. Undiagnosed and untreated OSA is also associated with a substantial economic burden, accounting for an estimated annual cost of $149.6 billion. Given the significant disease burden and economic costs associated with undiagnosed and untreated OSA, a robust clinical research framework is needed to address the prevention, diagnosis, and treatment of OSA. The P4 Medicine approach to Obstructive Sleep Apnea that drives this overall Program Project aims to conduct scientific studies that predict who will develop the disorder, prevent OSA and its adverse consequences, and personalize diagnoses, therapies, and clinical care. In order to study OSA through the Project in this Program, diverse and complex data from a variety of data sources, including the electronic health record (EHR), daily continuous positive airway pressure (CPAP) usage and adherence information, genetics, and molecular biomarkers must be studied. To effectively answer the scientific questions posed by each Project, we have constructed a Data Integration Core (Core C) to collect, integrate, and analyze information derived from these varied data sources. Our team will be led by Director Dr. John Holmes, an established medical informatician and epidemiologist with over 35 years of experience in database design and implementation, development of information systems for clinical research, and analytic collaborations with biostatisticians and clinical researchers. Core C will be co-led by co-Directors Dr. Danielle Mowery, a clinical informatician with expertise in data science, machine learning, and clinical research informatics, and Dr. Diego Mazzotti, a sleep medicine researcher with specific training in biomedical informatics. The Core C leadership and support staff bring decades of experience in clinical research, sleep medicine, data science, informatics, digital health, genome science, epidemiology, and biostatistics. Collectively, Core C will provide data collection and management resources, as well as development and incorporation of cutting-edge approaches to data integration and analysis to support all Projects. We will also implement strategies related to all stages of clinical research data management, including clinical database mining, integration of heterogeneous data sources, and data representation according to Findable, Accessible, Interoperable and Reusable (FAIR) principles, making all tools and approaches open-source and available to the biomedical research community at large.