Machine learning model predicts short-term mortality among prehospital patients: A prospective development study from Finland.

ABSTRACT:

Aim

To show whether adding blood glucose to the National Early Warning Score (NEWS) parameters in a machine learning model predicts 30-day mortality more precisely than the standard NEWS in a prehospital setting.

Methods

In this study, vital sign data prospectively collected from 3632 unselected prehospital patients in June 2015 were used to compare the standard NEWS to random forest models for predicting 30-day mortality. The NEWS parameters and blood glucose levels were used to develop the random forest models. Predictive performance on an unknown patient population was estimated with a ten-fold stratified cross-validation method.

Results

All NEWS parameters and blood glucose levels were reported in 2853 (79%) eligible patients. Within 30 days after contact with amb

SUBMITTER: Tamminen J 

PROVIDER: S-EPMC8244527 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature