The National Science Foundation (NSF) https://www.nsf.gov awarded a $1,182,305 grant to the University of Pittsburgh’s, Swanson, School of Engineering https://www.engineering.pitt.edu to use Machine Learning and Big Data to analyze electronic anesthesia records to prevent postoperative complications and death.
Dr. Heng Huang, Professor in Computer Engineering at the University of Pittsburgh and Principal Investigator on the study, will analyze more than two million cases of anesthesia data taken from 300 UPMC clinics and treatment centers.
As Dr. Huang explains, “Doctors use guidelines from manuals in combination with subjective experience to determine patients’ risk factors and needs. We are now going to use artificial intelligence and machine learning to develop an objective way to predict surgical outcomes based on historical patient data.”
Today, many patients come into the hospital with so much information in the file that doctors don’t always have a comprehensive way to consider all the variables in their electronic records. So many think that a computer can do a better job than a human to predict patient outcomes by employing emerging computational technologies including deep learning, semi-supervised learning, and large-scale optimization.