Prediction of Prolonged Length of Hospital Stay After Cancer Surgery Using Machine Learning on Electronic Health Records: Retrospective Cross-sectional Study.

ABSTRACT:

Background

Postoperative length of stay is a key indicator in the management of medical resources and an indirect predictor of the incidence of surgical complications and the degree of recovery of the patient after cancer surgery. Recently, machine learning has been used to predict complex medical outcomes, such as prolonged length of hospital stay, using extensive medical information.

Objective

The objective of this study was to develop a prediction model for prolonged length of stay after cancer surgery using a machine learning approach.

Methods

In our retrospective study, electronic health records (EHRs) from 42,751 patients who underwent primary surgery for 17 types of cancer between January 1, 2000, and December 31, 2017, were sourced from a single cancer center.

SUBMITTER: Jo YY 

PROVIDER: S-EPMC7939945 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature