University of Wisconsin
School of Medicine and Public Health

Matthew Churpek, MD, MPH, PhD

ASSOCIATE PROFESSOR

PULMONARY & CRITICAL CARE Faculty

UW MEDICAL FOUNDATION
8007 EXCELSIOR DR
MADISON, WI 53717-1903

Education

  • Duke University School of Medicine, Durham, North Carolina - MD

  • University of North Carolina Chapel Hill, Chapel Hill, North Carolina - MPH

  • University of Chicago Medical Center, Chicago, Illinois - Residency in Internal Medicine

  • University of Chicago Medical Center - Chief Resident

  • University of Chicago Medical Center - Fellowship in Pulmonary and Critical Care

  • University of Chicago - PhD in Epidemiology

Professional Activities

Dr. Matthew Churpek is a faculty member in the Division of Allergy, Pulmonary and Critical Care Medicine within the Department of Medicine. He is a board-certified clinical informaticist whose research program focuses on developing and implementing prediction models to detect early clinical deterioration in order to improve patient outcomes. Before joining UW, Dr. Churpek was a faculty member at the University of Chicago Medicine where he was inducted into the Inaugural Class of Faculty and Innovators by the Center for HealthCare Delivery Science and Innovation at the University of Chicago. Other awards include The American Society for Clinical Investigation Young Physician-Scientist Award, the American Thoracic Society (ATS) Foundation Recognition Award for Early Career Investigators, ATS Fellow, Inaugural Class, and the ATS Assembly on Critical Care Early Career Achievement Award.

Clinical Specialties

Dr. Churpek’s clinical work focuses on the diagnosis and treatment of critical illness, and he works as a critical care attending in the Trauma and Life Support Center (TLC). He is boarded in internal medicine, pulmonary medicine, critical care medicine, and clinical informatics. 

Research Interests

Search for Dr. Churpek’s research abstracts on PubMed

Dr. Churpek's data science laboratory utilizes electronic health record data and machine learning techniques, such as natural language processing and deep learning, to identify patients at risk for clinical deterioration, sepsis, acute kidney injury, and other syndromes of critical illness. His research has been supported by a K08 from NHLBI, an ATS Foundation Recognition Award for Early Career Investigators, and an R01 from NIGMS.