Differential Performance of Machine Learning Models in Prediction of Procedure-Specific Outcomes.

ABSTRACT:

Background

Procedure-specific complications can have devastating consequences. Machine learning-based tools have the potential to outperform traditional statistical modeling in predicting their risk and guiding decision-making. We sought to develop and compare deep neural network (NN) models, a type of machine learning, to logistic regression (LR) for predicting anastomotic leak after colectomy, bile leak after hepatectomy, and pancreatic fistula after pancreaticoduodenectomy (PD).

Methods

The colectomy, hepatectomy, and PD National Surgical Quality Improvement Program (NSQIP) databases were analyzed. Each dataset was split into training, validation, and testing sets in a 60/20/20 ratio, with fivefold cross-validation. Models were created using NN and LR for each outcome. Mo

SUBMITTER: Chen KA 

PROVIDER: S-EPMC9444966 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature