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.