Development and Validation of Machine Learning Models to Predict Readmission After Colorectal Surgery.

ABSTRACT:

Background

Readmission after colorectal surgery is common and often implies complications for patients and costs for hospitals. Previous works have created predictive models using logistic regression for this outcome but have shown limited accuracy. Machine learning has shown promise in improving predictions by identifying non-linear patterns in data. We sought to create a more accurate predictive model for readmission after colorectal surgery using machine learning.

Methods

Patients who underwent colorectal surgery were identified in the National Quality Improvement Program (NSQIP) database including years 2012-2019 and split into training, validation, and test sets. The primary outcome was readmission within 30 days of surgery. Three types of machine learning models were c

SUBMITTER: Chen KA 

PROVIDER: S-EPMC10081888 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature