A New Time-Window Prediction Model For Traumatic Hemorrhagic Shock Based on Interpretable Machine Learning.

ABSTRACT:

Abstract

Early warning prediction of traumatic hemorrhagic shock (THS) can greatly reduce patient mortality and morbidity. We aimed to develop and validate models with different stepped feature sets to predict THS in advance. From the PLA General Hospital Emergency Rescue Database and Medical Information Mart for Intensive Care III, we identified 604 and 1,614 patients, respectively. Two popular machine learning algorithms (i.e., extreme gradient boosting [XGBoost] and logistic regression) were applied. The area under the receiver operating characteristic curve (AUROC) was used to evaluate the performance of the models. By analyzing the feature importance based on XGBoost, we found that features in vital signs (VS), routine blood (RB), and blood gas analysis (BG) were the most rele

SUBMITTER: Zhao Y 

PROVIDER: S-EPMC8663521 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature