Identification of postoperative complications using electronic health record data and machine learning

Abstract Summary; Using the American College of Surgeons National Surgical Quality Improvement Program (NSQIP) complication status of patients who underwent an operation at the University of Colorado Hospital, we developed a machine learning algorithm for identifying patients with one or more complications using data from the electronic health record (EHR). The model achieved 88% specificity, 83% sensitivity, 97% negative predictive value, 52% positive predictive value, and an area under the curve of 0.93. The model developed could be used for electronic postoperative complication surveillance to supplement manual chart review.

Source: American Journal of Surgery - October 8, 2019 Category: Surgery Authors: Michael Bronsert, Abhinav B. Singh, William G. Henderson, Karl Hammermeister, Robert A. Meguid, Kathryn L. Colborn Source Type: research