BD2K Project

"Is Big Data Bigger than Its Own Hype?" Yale Insights, July 03, 2017

"Delayed Response to Antihypertension Medication: A Harbinger of Stroke, Heart Failure, and Vascular Disease" Hypertension Editorial Commentary, May 30, 2017

"Analyzing Patient Heterogeneity Upon Antihypertensive Therapy Initiation to Personalize Prognosis" Cardiology Advisor, March 22, 2017

"Can Big Data Cure Cancer?" U.S. News & World Report, October 15, 2014

"Big Data Destined for the Bedside Within 5 Years" Cardiology News, August 7, 2014

"Why Can't We Do Better Than This" The Health Care Blog, September 22, 2013

"Can Big Data Make Healthcare More Effective?" Yale School of Medicine, Yale Insights (video), February 21, 2013

"Forbes Healthcare Summit: Using Big Data to Make Patients Better" Forbes, February 5, 2013

Huang C, Murugiah K, Mahajan S, Li S-X, Dhurva SS, Haimovich JS, Wang Y, Schulz WL, Testani JM, Wilson FP, Mena CI, Masuoudi FA, Rumsfeld JS, Spertus JA, Mortazavi BJ, Krumholz HM. Enhancing the Prediction of Acute Kidney Injury Risk after Percutaneous Coronary Intervention Using Machine Learning Techniques: A Retrospective Cohort Study. PLOS Med. 2018. doi.org/10.1371/journal.pmed.1002703

Bates J, Parzynski CS, Dhruva SS, Coppi A, Kuntz R, Li S-X, Marinac-Dabic D, Masoudi FA, Shaw RE, Warner F, Krumholz HM, Ross JS. Quantifying the Utilization of Medical Devices Necessary to Detect Postmarket Safety Differences: A Case Study of Implantable Cardioverter Defibrillators. Pharmacoepidemiol Drug Saf. In Press

Cloninger A, Coifman RR, Downing N, Krumholz HM. Bigeometric organization of deep nets. Appl Comput Harmon Anal. 2018 Apr;44:774–785.

Au B, Shaham U, Dhruva S, Coppi A, Warner F, Cristea E, Bouras G, Lansky A, Li S-X, Schulz W, Krumholz HM. Deep Learning Segmentation of Coronary Angiograms in Quantitative Coronary Analysis. Yale School of Medicine Dean's Workshop: Inauguration of the Yale Center for Biomedical Data Science. February 7, 2018.

Durant TJS, Linderman GC, Freedman IG, Krumholz HM, Schulz WL. Comparison of Supervised and Unsupervised Techniques for Computer Assisted Decision Support in Medical Imaging. Yale School of Medicine Dean's Workshop: Inauguration of the Yale Center for Biomedical Data Science. February 7, 2018.

Freedman IG, Durant TJS, Krumholz HM, Schulz WL. Automated Feature Segmentation in Digital Cervigrams with a Discriminative Convolutional Neural Network . Yale School of Medicine Dean's Workshop: Inauguration of the Yale Center for Biomedical Data Science. February 7, 2018.

Haimovich JS, Huang C, Li S-X, Mortazavi BJ, Krumholz HM. Prediction of In-Hospital Mortality with Acute Myocardial Infarction using Machine Learning Techniques. Yale School of Medicine Dean's Workshop: Inauguration of the Yale Center for Biomedical Data Science. February 7, 2018.

Li Y, Krumholz HM, Schulz WL. Assessment of Content and Structure of Clinical Notes at an Academic Medical Center . Yale School of Medicine Dean's Workshop: Inauguration of the Yale Center for Biomedical Data Science. February 7, 2018.

Schulz WL, Young HP, Dai H, Durant TJS, Jiang L, Krumholz HM. TrialChain: A Blockchain Implementation for Decentralized Governance of Clinical Trial Data. Yale School of Medicine Dean's Workshop: Inauguration of the Yale Center for Biomedical Data Science. February 7, 2018.

Young HP, Byron WM, Torre Jr CJ, Jiang G, Shah ND, Krumholz HM, Ross JS, Schulz WL. Validation of a Common Data Model for Clinical Research and Post Market Surveillance. Yale School of Medicine Dean's Workshop: Inauguration of the Yale Center for Biomedical Data Science. February 7, 2018.

Huang, C, Dhruva SS, Coppi AC, Warner F, Li S-X, Lin H, Nasir K, Krumholz HM. Systolic Blood Pressure Response in SPRINT (Systolic Blood Pressure Intervention Trial) and ACCORD (Action to Control Cardiovascular Risk in Diabetes): A Possible Explanation for Discordant Trial Results. J Am Heart Assoc. 2017;6:e007509. DOI: 10.1161/JAHA.117.007509

Ross JS, Bates J, Parzynski CS, Akar JG, Curtis JP, Desai NR, Freeman JV, Gamble GM, Kuntz R, Li SX, Marinac-Dabic D, Masoudi FA, Normand SLT, Ranasinghe I, Shaw RE, Krumholz HM. Can machine learning complement traditional medical device surveillance? A case-study of dual-chamber implantable cardioverter–defibrillators. Medical Devices: Evidence and Research. 2017 August (10):165-188. DOI: 10.2147/MDER.S138158

Dhurva SS, Huang C, Spatz ES, Coppi A, Warner F, Li S-X, Lin H, Xu X, Furberg CD, Davis BR, Pressel SL, Coifman RR, Krumholz HK. Heterogeneity in early responses in ALLHAT (Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial). Hypertension. 2017 Jul;70(1):94-102.

Mortazavi BJ, Desai N, Zhang J, Coppi A, Warner F, Krumholz HM, Negahban S. Prediction of adverse events in patients undergoing major cardiovascular procedures. IEEE Journal of Biomedical and Health Informatics. 2017; DOI: 10.1109/JBHI.2017.2675340

Haimovich JS, Venkatesh AK, Shojaee A, Coppi A, Warner F, Li SX, Krumholz HM. Discovery of temporal and disease association patterns in condition-specific hospital utilization rates. PLOS ONE. 2017; 12(3):e0172049. doi: 10.1371/journal.pone.0172049.

Mortazavi BJ, Downing NS, Bucholz EM, Dharmarajan K, Manhapra A, Li SX, Negahban SN, Krumholz HM. Analysis of machine learning techniques for heart failure readmissions. Circ Cardiovasc Qual Outcomes. 2016;9:DOI: 10.1161/CIRCOUTCOMES.116.003039.

Krumholz HM. The promise of big data: opportunities and challenges. Circ Cardiovasc Qual Outcomes. 2016;9:DOI: 10.1161/CIRCOUTCOMES.116.003366.

Krumholz HM. Big data and new knowledge in medicine: the thinking, training, and tools needed for a learning health system. Health Affairs. 2014;33:1163-1170.