Validation of deep-learning-based triage and acuity score using a large national dataset

Kwon, Joon‐myoung; Lee, Youngnam; Lee, Yeha; Lee, Seung-Woo; Park, Soo Young; Park, Jinsik

doi:10.1371/journal.pone.0205836

Cited by 68 publications

(80 citation statements)

References 34 publications

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“…‡The alternative hypothesis for this p-value was that there is a difference between the ensemble model, combining artificial intelligence and the ESI, and the other predictive methods Meanwhile, deep learning includes feature learning, which allows the model to automatically learn the relationships and characteristics between input variables required to perform a task [30]. As shown in our previous studies, deep learning could be used to understand the connection between features and outperformed conventional and other machine learning methods [9,11,31]. It is important to note that feature learning is not designed by humans in deep learning.…”

Section: Discussionmentioning

confidence: 98%

“…Deep learning can obtain a high performance without prior knowledge to train the model; thus, indicating that deep learning somehow automatically learns the feature relationship among input variables. In our previous study, we developed an AI algorithm based on deep learning for predicting the critical care of patients in an ED [11]. From the previous study, we found that conventional statistical methods such as logistic regression may have difficulty in determining the relationship among input variables [10,28,29].…”

Section: Discussionmentioning

confidence: 99%

“…The run sheets were saved as electronic medical records. The sample size of the validation dataset was determined using an accurate algorithm in a previous study [11].…”

Section: Study Design and Settingmentioning

confidence: 99%

“…It is based on the severity of patients' healthcare problems and the number of resources that is anticipated to require. KTAS was developed in 2012 based on the Canadian Triage and Acuity Scale and has been used nationwide as a triage since 2016 in Korea [11,17,18]. KTAS is a five-level ED triage algorithm that considers symptoms, pain, and physiological values.…”

Section: Performance Test Of Ai Algorithm and Comparison With Conventmentioning

confidence: 99%

“…The AUC is a frequently used metric and shows the sensitivity against 1-specificity [22]. Based on previous studies, we used levels 1-2, levels 1-2, points 3-14, and points 5-20 to predict the critical care with the ESI, KTAS, MEWS, and NEWS, respectively [11,[17][18][19][20][21]. When evaluating the continuous score predicted by the AI algorithm, we fixed the sensitivity as 0.8.…”

Section: Performance Test Of Ai Algorithm and Comparison With Conventmentioning

confidence: 99%

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

Kang

Cho²,

Kwon³

et al. 2020

Scand J Trauma Resusc Emerg Med

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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.

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Section: Discussionmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

“…The run sheets were saved as electronic medical records. The sample size of the validation dataset was determined using an accurate algorithm in a previous study [11].…”

Section: Study Design and Settingmentioning

confidence: 99%

Section: Performance Test Of Ai Algorithm and Comparison With Conventmentioning

confidence: 99%

Section: Performance Test Of Ai Algorithm and Comparison With Conventmentioning

confidence: 99%

See 3 more Smart Citations

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

Kang

Cho²,

Kwon³

et al. 2020

Scand J Trauma Resusc Emerg Med

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View full text Add to dashboard Cite

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Predicting cardiac arrest in the emergency department

2020

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In‐hospital cardiac arrest remains a leading cause of death: roughly 300,000 in‐hospital cardiac arrests occur each year in the United States, ≈10% of which occur in the emergency department. ED‐based cardiac arrest may represent a subset of in‐hospital cardiac arrest with a higher proportion of reversible etiologies and a higher potential for neurologically intact survival. Patients presenting to the ED have become increasingly complex, have a high burden of critical illness, and face crowded departments with thinly stretched resources. As a result, patients in the ED are vulnerable to unrecognized clinical deterioration that may lead to ED‐based cardiac arrest. Efforts to identify patients who may progress to ED‐based cardiac arrest have traditionally been approached through identification of critically ill patients at triage and the identification of patients who unexpectedly deteriorate during their stay in the ED. Interventions to facilitate appropriate triage and resource allocation, as well as earlier identification of patients at risk of deterioration in the ED, could potentially allow for both prevention of cardiac arrest and optimization of outcomes from ED‐based cardiac arrest. This review will discuss the epidemiology of ED‐based cardiac arrest, as well as commonly used approaches to predict ED‐based cardiac arrest and highlight areas that require further research to improve outcomes for this population.

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Artificial intelligence for emergency medical care

Rajput,

Sharma,

Malviya

2023

Health Care Science

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There is increasing research into the potential benefits of incorporating artificial intelligence (AI) and machine learning algorithms into emergency medical services. AI is finding new applications across a wide range of sectors, one of which is healthcare, where it is being used to enhance clinical diagnostics. AI solutions have enormous untapped potential to improve healthcare efficiency and quality, thus researchers have focused heavily on emergency medicine (EM). Many individuals without prior experience with any physician often receive their initial medical care in the emergency room. Two areas that could benefit from the implementation of AI are reducing waiting times and enhancing diagnostic capabilities. This study provides further explanation of how AI is used in emergency rooms. Several machine learning‐based algorithms are also addressed. In this research, we summarise recent developments in the use of AI in EM. This research tries to summarise the usefulness of AI in EM by looking at recent developments in emergency department operations and clinical patient management.

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Validation of deep-learning-based triage and acuity score using a large national dataset

Cited by 68 publications

References 34 publications

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

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

Predicting cardiac arrest in the emergency department

Artificial intelligence for emergency medical care

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