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

Background 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 de...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Scandinavian journal of trauma, resuscitation and emergency medicine Jg. 28; H. 1; S. 17 - 8
Hauptverfasser: Kang, Da-Young, Cho, Kyung-Jae, Kwon, Oyeon, Kwon, Joon-myoung, Jeon, Ki-Hyun, Park, Hyunho, Lee, Yeha, Park, Jinsik, Oh, Byung-Hee
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London BioMed Central 04.03.2020
BioMed Central Ltd
Springer Nature B.V
BMC
Schlagworte:
Algorithms
Artificial intelligence
Blood pressure
Body temperature
Critical care
Critical care medicine
Deep learning
Emergency medical care
Emergency medical service
Emergency medical services
Emergency Medicine
Emergency services
Heart rate
Hospital emergency services
Hospitals
Medical care needs assessment
Medical research
Medicine
Medicine & Public Health
Methods
Original Research
Patients
Technology application
Time
Traumatic Surgery
Triage
Vital signs
South Korea
Deep learning
Triage
Emergency medical service
Artificial intelligence
ISSN:1757-7241, 1757-7241
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Background 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. Methods 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. Results 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 (0.839 [0.831–0.846]), Korean Triage and Acuity System (0.824 [0.815–0.832]), National Early Warning Score (0.741 [0.734–0.748]), and Modified Early Warning Score (0.696 [0.691–0.699]). Conclusions The AI algorithm accurately predicted the need for the critical care of patients using information during EMS and outperformed the conventional triage tools and early warning scores.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ObjectType-Undefined-3
ISSN:1757-7241
1757-7241
DOI:10.1186/s13049-020-0713-4