Artificial intelligence decision points in an emergency department

Chang, Hansol; Chul, Won

doi:10.15441/ceem.22.366

Cited by 9 publications

(8 citation statements)

References 37 publications

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“…A previous study reported the use of serial vital signs in predicting patient status in intensive care units (ICUs) or wards ( 13 ). However, few studies have explored the use of sequential vital-sign information in EDs ( 14 ).…”

Section: Introductionmentioning

confidence: 99%

Early Prediction of Unexpected Latent Shock in the Emergency Department Using Vital Signs

Chang,

Jung,

et al. 2023

Shock

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Objective/Introduction Sequential vital-sign information and trends in vital signs are useful for predicting changes in patient state. This study aims to predict latent shock by observing sequential changes in patient vital signs. Methods The dataset for this retrospective study contained a total of 93,194 emergency department (ED) visits from January 1, 2016, and December 31, 2020, and Medical Information Mart for Intensive Care (MIMIC)-IV-ED data. We further divided the data into training and validation datasets by random sampling without replacement at a 7:3 ratio. We carried out external validation with MIMIC-IV-ED. Our prediction model included logistic regression (LR), random forest (RF) classifier, a multi-layer perceptron (MLP), and a recurrent neural network (RNN). To analyze the model performance, we used area under the receiver operating characteristic curve (AUROC). Results Data of 89,250 visits of patients who met pre-specified criteria were used to develop a latent-shock prediction model. Data of 142,250 patient visits from MIMIC-IV-ED satisfying the same inclusion criteria were used for external validation of the prediction model. The AUROC values of prediction for latent shock were 0.822, 0.841, 0.852, and 0.830 with RNN, MLP, RF, and LR methods, respectively, at 3 h before latent shock. This is higher than the shock index or adjusted shock index. Conclusion We developed a latent shock prediction model based on 24 hours of vital-sign sequence that changed with time and predicted the results by individual.

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Section: Introductionmentioning

confidence: 99%

Early Prediction of Unexpected Latent Shock in the Emergency Department Using Vital Signs

Chang,

Jung,

et al. 2023

Shock

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“…Artificial intelligence (AI) and machine learning (ML) are powerful technologies that have the potential to improve medical care [1]. AI refers to the broader concept of technology being able to carry out tasks in an autonomous and smart way, encompassing a variety of technologies, while ML is a subset of AI focused on the idea that machines can learn from data, identify patterns, and make decisions with minimal human intervention [1][2][3][4]. Particularly in emergency medicine, AI and ML are expected to play critical roles in accelerating triage, diagnosis, and prognostication to optimize individual patient care through the input of clinical information and/or image recognition [2,[4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Explainable artificial intelligence in emergency medicine: an overview

Okada,

Ning,

Ong

2023

Clin Exp Emerg Med

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Artificial intelligence (AI) and machine learning (ML) have potential to revolutionize emergency medical care by enhancing triage systems, improving diagnostic accuracy, refining prognostication, and optimizing various aspects of clinical care. However, as clinicians often lack AI expertise, they might perceive AI as a “black box,” leading to trust issues. To address this, “explainable AI,” which teaches AI functionalities to end-users, is important. This review presents the definitions, importance, and role of explainable AI, as well as potential challenges in emergency medicine. First, we introduce the terms explainability, interpretability, and transparency of AI models. These terms sound similar but have different roles in discussion of AI. Second, we indicate that explainable AI is required in clinical settings for reasons of justification, control, improvement, and discovery and provide examples. Third, we describe three major categories of explainability: pre-modeling explainability, interpretable models, and post-modeling explainability and present examples (especially for post-modeling explainability), such as visualization, simplification, text justification, and feature relevance. Last, we show the challenges of implementing AI and ML models in clinical settings and highlight the importance of collaboration between clinicians, developers, and researchers. This paper summarizes the concept of “explainable AI” for emergency medicine clinicians. This review may help clinicians understand explainable AI in emergency contexts.

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“…Several studies have applied deep learning (DL) or other artificial intelligence-based models on triage acuity scales for better prediction [ 12 , [20] , [21] , [22] , [23] ]. These studies on DL-based triage and acuity scores predicted in-hospital mortality and hospitalization during triage [ 20 , 22 , [24] , [25] , [26] , [27] , [28] ]. However, there have been a few attempts to predict miss-triaged patients at triage.…”

Section: Introductionmentioning

confidence: 99%

Clinical support system for triage based on federated learning for the Korea triage and acuity scale

Chang,

Yu,

Lee

et al. 2023

Heliyon

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Artificial intelligence decision points in an emergency department

Cited by 9 publications

References 37 publications

Early Prediction of Unexpected Latent Shock in the Emergency Department Using Vital Signs

Early Prediction of Unexpected Latent Shock in the Emergency Department Using Vital Signs

Explainable artificial intelligence in emergency medicine: an overview

Clinical support system for triage based on federated learning for the Korea triage and acuity scale

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