Machine Learning Applications in Acute Pain Medicine

Date(s) - 10/17/2016
3:00 pm

Patrick James Tighe, M.D., MS., Associate Professor of Anesthesiology, Joint Assistant Professor of Orthopaedics, Joint Assistant Professor of Information Systems and Operations Management, University of Florida

Acute postoperative pain affects nearly all surgical patients, and the treatment of this pain may carry important implications for personal and public health. This discussion will review the basic epidemiology of acute pain, and discuss various machine learning approaches by our team to better characterize acute postoperative pain.

Short Bio:
Dr. Tighe serves in the Division of Acute Pain Medicine and provides care in the blockroom, acute pain service, and operating room. Additionally, he is the director of the Perioperative Analytic Group. This team applies advanced analytical techniques in an operational clinical environment to improve perioperative data utilization.

Dr. Tighe’s research examines how machine learning algorithms, stochastic process modeling, social network analyses and computer vision can improve processes related to acute postoperative pain and perioperative patient safety. His work is currently supported by a K23 from the National Institute of General Medical Sciences at the National Institutes of Health, and he was recently awarded an R01 slated to begin in July of 2015 to examine the temporal dynamics of postoperative pain. He collaborates with a diverse array of researchers from the Colleges of Engineering, Business, Dentistry, and Liberal Arts and Sciences.

Dr. Tighe serves as section co-editor for the Acute and Perioperative Section of the journal Pain Medicine, and is active in the American Academy of Pain Medicine’s Acute Pain Medicine Special Interest Group.