Artificial intelligence algorithm to predict the need for critical care in prehospital emergency medical services

Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine
Da-Young KangByung-Hee Oh

Abstract

In emergency medical services (EMSs), accurately predicting the severity of a patient's medical condition is important for the early identification of those who are vulnerable and at high-risk. In this study, we developed and validated an artificial intelligence (AI) algorithm based on deep learning to predict the need for critical care during EMS. We conducted a retrospective observation cohort study. The algorithm was established using development data from the Korean national emergency department information system, which were collected during visits in real time from 151 emergency departments (EDs). We validated the algorithm using EMS run sheets from two EDs. The study subjects comprised adult patients who visited EDs. The endpoint was critical care, and we used age, sex, chief complaint, symptom onset to arrival time, trauma, and initial vital signs as the predicted variables. The number of patients in the development data was 8,981,181, and the validation data comprised 2604 EMS run sheets from two hospitals. The area under the receiver operating characteristic curve of the algorithm to predict the critical care was 0.867 (95% confidence interval, [0.864-0.871]). This result outperformed the Emergency Severity Index (0.8...Continue Reading

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Citations

Sep 24, 2020·Revista da Associação Médica Brasileira·Nedy M B C NevesAlmir G V Bitencourt
Oct 13, 2020·NPJ Digital Medicine·Bertalan Meskó, Marton Görög
Apr 14, 2021·JAMA Network Open·Francisco Martín-RodríguezAncor Sanz-García
Apr 29, 2021·Trauma Surgery & Acute Care Open·Tareq KheirbekCharles A Adams
May 21, 2021·Healthcare Informatics Research·Hun-Sung KimWon Chul Cha
Jun 22, 2021·BMC Medical Informatics and Decision Making·Anna LarssonMartin Gerdin Wärnberg
Nov 2, 2020·The American Journal of Emergency Medicine·Haley EhrlichAdel Elkbuli
Aug 1, 2020·Critical Care : the Official Journal of the Critical Care Forum·Jesper JohnssonAttila Frigyesi
Aug 15, 2021·The American Journal of Emergency Medicine·Sruthi SelvakumarAdel Elkbuli

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Methods Mentioned

BETA
feature extraction
biopsy

Software Mentioned

ROCR
Google Brain
XGboost
TensorFlow
CatBoost
R R Development Core Team

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