Prediction of inappropriate pre-hospital transfer of patients with suspected cardiovascular emergency diseases using machine learning: a retrospective observational study.

ABSTRACT:

Background

This study aimed to develop a prediction model for transferring patients to an inappropriate hospital for suspected cardiovascular emergency diseases at the pre-hospital stage, using variables obtained from an integrated nationwide dataset, and to assess the performance of this model.

Methods

We integrated three nationwide datasets and developed a two-step prediction model utilizing a machine learning algorithm. Ninety-eight clinical characteristics of patients identified at the pre-hospital stage and 13 hospital components were used as input data for the model. The primary endpoint of the model was the prediction of transfer to an inappropriate hospital.

Results

A total of 94,256 transferred patients in the public pre-hospital care system matched the Natio

SUBMITTER: Kim JH 

PROVIDER: S-EPMC10080868 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature