A machine-learning model to predict postoperative delirium following knee arthroplasty using electronic health records.

ABSTRACT:

Background

Postoperative delirium is a challenging complication due to its adverse outcome such as long hospital stay. The aims of this study were: 1) to identify preoperative risk factors of postoperative delirium following knee arthroplasty, and 2) to develop a machine-learning prediction model.

Method

A total of 3,980 patients from two hospitals were included in this study. The model was developed and trained with 1,931 patients from one hospital and externally validated with 2,049 patients from another hospital. Twenty preoperative variables were collected using electronic hospital records. Feature selection was conducted using the sequential feature selection (SFS). Extreme Gradient Boosting algorithm (XGBoost) model as a machine-learning classifier was applied to predi

SUBMITTER: Jung JW 

PROVIDER: S-EPMC9235137 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature