Frederic S. Resnic

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iDASH (integrating data for analysis, anonymization, and sharing) is the newest National Center for Biomedical Computing funded by the NIH. It focuses on algorithms and tools for sharing data in a privacy-preserving manner. Foundational privacy technology research performed within iDASH is coupled with innovative engineering for collaborative tool(More)
OBJECTIVES Using a local percutaneous coronary intervention (PCI) data repository, we sought to compare the performance of a number of local and well-known mortality models with respect to discrimination and calibration. BACKGROUND Accurate risk prediction is important for a number of reasons including physician decision support, quality of care(More)
Support vector machines (SVM) have become popular among machine learning researchers, but their applications in biomedicine have been somewhat limited. A number of methods, such as grid search and evolutionary algorithms, have been utilized to optimize model parameters of SVMs. The sensitivity of the results to changes in optimization methods has not been(More)
OBJECTIVE A variety of postmarketing surveillance strategies to monitor the safety of medical devices have been supported by the U.S. Food and Drug Administration, but there are few systems to automate surveillance. Our objective was to develop a system to perform real-time monitoring of safety data using a variety of process control techniques. DESIGN(More)
OBJECTIVE Medical classification accuracy studies often yield continuous data based on predictive models for treatment outcomes. A popular method for evaluating the performance of diagnostic tests is the receiver operating characteristic (ROC) curve analysis. The main objective was to develop a global statistical hypothesis test for assessing the(More)
BACKGROUND Automated adverse outcome surveillance tools and methods have potential utility in quality improvement and medical product surveillance activities. Their use for assessing hospital performance on the basis of patient outcomes has received little attention. We compared risk-adjusted sequential probability ratio testing (RA-SPRT) implemented in an(More)
Data Extraction and Longitudinal Time Analysis (DELTA) was developed to provide real-time safety monitoring of new devices in the domain of interventional cardiology. This field provides the necessary infrastructure for this type of endeavor. The American College of Cardiology National Cardiovascular Data Repository (ACC-NCDR) provides a national(More)
Prospective outcomes surveillance using population level data allows for statistical methodologies and confounder adjustment not supported by the FDA's current monitoring system. We explored propensity score matching integrated into an automated surveillance tool as a method for confounder adjustment in an observational cohort. The application analyzed all(More)
BACKGROUND Centralized and federated models for sharing data in research networks currently exist. To build multivariate data analysis for centralized networks, transfer of patient-level data to a central computation resource is necessary. The authors implemented distributed multivariate models for federated networks in which patient-level data is kept at(More)
BACKGROUND Access site complications contribute to morbidity and mortality during percutaneous coronary intervention (PCI). Transradial arterial access significantly lowers the risk of access site complications compared to transfemoral arteriotomy. We sought to develop a prediction model for access site complications in patients undergoing PCI with femoral(More)