Dynamic Predictions of Postoperative Complications from Explainable, Uncertainty-Aware, and Multi-Task Deep Neural Networks

no code implementations • 27 Apr 2020 • Benjamin Shickel, Tyler J. Loftus, Matthew Ruppert, Gilbert R. Upchurch, Tezcan Ozrazgat-Baslanti, Parisa Rashidi, Azra Bihorac

In a longitudinal cohort study of 56, 242 patients undergoing 67, 481 inpatient surgical procedures at a university medical center, we compared deep learning models with random forests for predicting nine common postoperative complications using preoperative, intraoperative, and perioperative patient data.

Decision Making Multi-Task Learning +2