Prediction Model of Anastomotic Leakage Among Esophageal Cancer Patients After Receiving an Esophagectomy: Machine Learning Approach.

ABSTRACT:

Background

Anastomotic leakage (AL) is one of the severe postoperative adverse events (5%-30%), and it is related to increased medical costs in cancer patients who undergo esophagectomies. Machine learning (ML) methods show good performance at predicting risk for AL. However, AL risk prediction based on ML models among the Chinese population is unavailable.

Objective

This study uses ML techniques to develop and validate a risk prediction model to screen patients with emerging AL risk factors.

Methods

Analyses were performed using medical records from 710 patients who underwent esophagectomies at the National Clinical Research Center for Cancer between January 2010 and May 2015. We randomly split (9:1) the data set into a training data set of 639 patients and a testing

SUBMITTER: Zhao Z 

PROVIDER: S-EPMC8367102 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature