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

Kang, Da-Young ; Cho, Kyung-Jae ; Kwon, Oyeon ; Kwon, Joon-Myoung ; Jeon, Ki-Hyun ; Park, Hyunho ; Lee, Yeha ; Park, Jinsik ; Oh, Byung-Hee

Scandinavian journal of trauma, resuscitation and emergency medicine, 2020-03, Vol.28 (1), p.17-17, Article 17 [Periódico revisado por pares]

England: BioMed Central Ltd

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Título:
Artificial intelligence algorithm to predict the need for critical care in prehospital emergency medical services
Autor: Kang, Da-Young ; Cho, Kyung-Jae ; Kwon, Oyeon ; Kwon, Joon-Myoung ; Jeon, Ki-Hyun ; Park, Hyunho ; Lee, Yeha ; Park, Jinsik ; Oh, Byung-Hee
Assuntos: Algorithms ; Artificial intelligence ; Blood pressure ; Body temperature ; Critical care ; Critical care medicine ; Deep learning ; Emergency medical care ; Emergency medical service ; Emergency medical services ; Emergency service ; Emergency services ; Heart rate ; Hospital emergency services ; Hospitals ; Medical care ; Medical care needs assessment ; Medical research ; Methods ; Needs assessment ; Original Research ; Patients ; Technology application ; Time ; Triage ; Vital signs
É parte de: Scandinavian journal of trauma, resuscitation and emergency medicine, 2020-03, Vol.28 (1), p.17-17, Article 17
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Descrição: 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 (0.839 [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]). 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.
Editor: England: BioMed Central Ltd
Idioma: Inglês

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

Kang, Da-Young ; Cho, Kyung-Jae ; Kwon, Oyeon ; Kwon, Joon-Myoung ; Jeon, Ki-Hyun ; Park, Hyunho ; Lee, Yeha ; Park, Jinsik ; Oh, Byung-Hee

England: BioMed Central Ltd

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