The applicant describes a 5-year career development program leading to independent academic research in biomedical networks under Marco Ramoni and co-mentors David Bates, Isaac Kohane, and Peter Szolovits. The applicant proposes a combined training and research program to lead to independence. Training involves biomedical methods underlying the networks being investigated by the applicant. On the research front, the hypothesis is that a holistic approach to the processing of information in biomedical networks is able to uncover novel types of relationships that could not be identified through current reductionistic methods.
The aims are: 1) Design and develop a methodological framework, based on statistical signal processing, able to analyze biomedical networks and discover hidden relationships. 2) Develop and implement a scalable, modular architecture that is able to adaptively integrate information into the framework described in aim 1. 3) Apply the architecture in specific aim 2 to the development of a system for the analysis of large-scale EHR-based clinical informatics networks.
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|Warner, Jeremy L; Denny, Joshua C; Kreda, David A et al. (2015) Seeing the forest through the trees: uncovering phenomic complexity through interactive network visualization. J Am Med Inform Assoc 22:324-9|
|Warner, Jeremy L; Zollanvari, Amin; Ding, Quan et al. (2013) Temporal phenome analysis of a large electronic health record cohort enables identification of hospital-acquired complications. J Am Med Inform Assoc 20:e281-7|
|Warner, Jeremy; Yang, Peter; Alterovitz, Gil (2013) Automated synthesis and visualization of a chemotherapy treatment regimen network. Stud Health Technol Inform 192:62-6|
|Warner, Jeremy L; Alterovitz, Gil; Bodio, Kelly et al. (2013) External phenome analysis enables a rational federated query strategy to detect changing rates of treatment-related complications associated with multiple myeloma. J Am Med Inform Assoc 20:696-9|
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