Using machine learning to improve the accuracy of patient deterioration predictions: Mayo Clinic Early Warning Score (MC-EWS).

ABSTRACT:

Objective

We aimed to develop a model for accurate prediction of general care inpatient deterioration.

Materials and methods

Training and internal validation datasets were built using 2-year data from a quaternary hospital in the Midwest. Model training used gradient boosting and feature engineering (clinically relevant interactions, time-series information) to predict general care inpatient deterioration (resuscitation call, intensive care unit transfer, or rapid response team call) in 24 hours. Data from a tertiary care hospital in the Southwest were used for external validation. C-statistic, sensitivity, positive predictive value, and alert rate were calculated for different cutoffs and compared with the National Early Warning Score. Sensitivity analysis evaluated predict

SUBMITTER: Romero-Brufau S 

PROVIDER: S-EPMC8661441 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature