Application of machine learning approaches to administrative claims data to predict clinical outcomes in medical and surgical patient populations.

Abstract

Authors

• Emily J MacKay
  Department of Anesthesiology and Critical Care, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
• Michael D Stubna
  Penn Predictive Healthcare, Penn Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
• Corey Chivers
  Penn Medicine, University of Pennsylvania, Philadelphia.
• Michael E Draugelis
  Penn Medicine, University of Pennsylvania, Philadelphia.
• William J Hanson
  Division of Cardiovascular Surgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
• Nimesh D Desai
• Peter W Groeneveld
  From the Department of Veterans Affairs' Center for Health Equity Research and Promotion, Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA (P.W.G.); Division of General Internal Medicine, Department of Medicine, Perelman School of Medicine, Leonard Davis Institute of Health Economics, and Center for Cardiovascular Outcomes, Quality, and Evaluative Research, University of Pennsylvania, Philadelphia (P.W.G.); University of Colorado School of Medicine, Aurora, CO (J.S.R.); Veterans Affairs Eastern Colorado Health System, Denver (J.S.R.); and American College of Cardiology, Washington, DC (J.S.R.). petergro@upenn.edu.

Keywords

External Resources