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David F.
Kong, M.D., A.M., F.A.C.C., F.S.C.A.I.
David F. Kong is an Assistant Professor of Medicine at Duke University
Medical Center and the Duke Clinical Research Institute. He is an
interventional cardiologist at Duke Hospital, specializing in cardiovascular
informatics research and integration of evidence from cardiovascular
clinical trials. Dr. Kong graduated magna cum laude from Harvard
University, where he also received a master's degree in Organismic
and Evolutionary Biology. He received his medical degree from the
Johns Hopkins University School of Medicine, and was a resident
on the Osler Medical Service at the Johns Hopkins Hospital. He completed
fellowships in Cardiovascular Disease and Interventional Cardiovascular
Medicine at Duke University before joining the Duke faculty.
Dr. Kong is board certified in internal medicine, cardiology, and
interventional cardiology. He has been elected Fellow of the American
College of Cardiology and the Society for Cardiovascular Angiography
and Interventions.
Dr. Kong's experience
with the Duke Cardiovascular Diseases Database (DCCD) began in 1985
and includes development and validation of statistical models for
coronary disease outcomes. He has written user interfaces for the
prognostigram software that incorporates multiple statistical models
for use for decision support and education. Dr. Kong has conducted
systematic overviews of glycoprotein IIb/IIIa receptor antagonists,
direct thrombin inhibition, and coronary stenting. Statistical work
with Dr. Vic Hasselblad has included construction of virtual placebo
arms for evaluation of active-control clinical studies.
He has directed the Machine Learning group at the Duke Clinical
Research Institute, with evaluation of neural network and rule induction
modeling strategies for predicting clinical and cost outcomes in
coronary artery disease.
In 2003, he received a Faculty Award from International Business
Machines Corporation (IBM) in recognition of his outstanding research.
Current projects include development of experience-based statistical
indices for survival based on the coronary arteriogram, and creation
of disease-state models for evaluating the impact of drug-eluting
coronary stent technology.
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