Development and validation of a deep neural network model to predict postoperative mortality, acute kidney injury, and reintubation using a single feature set.

ABSTRACT: During the perioperative period patients often suffer complications, including acute kidney injury (AKI), reintubation, and mortality. In order to effectively prevent these complications, high-risk patients must be readily identified. However, most current risk scores are designed to predict a single postoperative complication and often lack specificity on the patient level. In other fields, machine learning (ML) has been shown to successfully create models to predict multiple end points using a single input feature set. We hypothesized that ML can be used to create models to predict postoperative mortality, AKI, reintubation, and a combined outcome using a single set of features available at the end of surgery. A set of 46 features available at the end of surgery, including drug dosing, b

SUBMITTER: Hofer IS 

PROVIDER: S-EPMC7170922 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature