Clinical Decision Support Systems for Triage in the Emergency Department using Intelligent Systems: a Review

Fernandes, Marta; Vieira, Susana M.; Leite, Francisca Pais; Palos, Carlos; Finkelstein, Stan N.; Sousa, J.M.

doi:10.1016/j.artmed.2019.101762

Cited by 169 publications

(143 citation statements)

References 99 publications

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“…Recently, machine-learning models have been applied for predicting diverse outcomes in the ED, e.g., cardiac arrest prediction [24], ED triage [34][35][36], prediction of hospital admission [37], identification of patients with suspected infection [27], screening of sepsis [28] or septic shock [26], and mortality prediction in patients with sepsis [38] or suspected infection [39]. Our study suggests that the ability of machine-learning models for predicting deterioration within three days of patients with suspected infection are superior to the conventional severity illness scores.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Quick Sepsis-Related Organ Failure Assessment-Based Machine-Learning Model for Mortality Prediction in Patients with Suspected Infection in the Emergency Department

Kwon

Baek

2020

JCM

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The quick sepsis-related organ failure assessment (qSOFA) score has been introduced to predict the likelihood of organ dysfunction in patients with suspected infection. We hypothesized that machine-learning models using qSOFA variables for predicting three-day mortality would provide better accuracy than the qSOFA score in the emergency department (ED). Between January 2016 and December 2018, the medical records of patients aged over 18 years with suspected infection were retrospectively obtained from four EDs in Korea. Data from three hospitals (n = 19,353) were used as training-validation datasets and data from one (n = 4234) as the test dataset. Machine-learning algorithms including extreme gradient boosting, light gradient boosting machine, and random forest were used. We assessed the prediction ability of machine-learning models using the area under the receiver operating characteristic (AUROC) curve, and DeLong’s test was used to compare AUROCs between the qSOFA scores and qSOFA-based machine-learning models. A total of 447,926 patients visited EDs during the study period. We analyzed 23,587 patients with suspected infection who were admitted to the EDs. The median age of the patients was 63 years (interquartile range: 43–78 years) and in-hospital mortality was 4.0% (n = 941). For predicting three-day mortality among patients with suspected infection in the ED, the AUROC of the qSOFA-based machine-learning model (0.86 [95% CI 0.85–0.87]) for three -day mortality was higher than that of the qSOFA scores (0.78 [95% CI 0.77–0.79], p < 0.001). For predicting three-day mortality in patients with suspected infection in the ED, the qSOFA-based machine-learning model was found to be superior to the conventional qSOFA scores.

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Section: Discussionmentioning

confidence: 99%

Development and Validation of a Quick Sepsis-Related Organ Failure Assessment-Based Machine-Learning Model for Mortality Prediction in Patients with Suspected Infection in the Emergency Department

Kwon

Baek

2020

JCM

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“…The data abstraction process included the possibility of a patient being discharged and returning to the ED as critically ill within 24 hours. There have been many different measures of resource use and illness severity used across studies evaluating the efficacy of triage and machine‐learning predictions, including specific diagnoses such as sepsis, 8 admission, 7 and overall mortality 10 . The composite of mortality and ICU admission is an appealing compromise metric, as it identifies a population that is more likely to require rapid intervention than the larger population of patients needing admission.…”

Section: Methodsmentioning

confidence: 99%

Deep‐learning approaches to identify critically Ill patients at emergency department triage using limited information

et al. 2020

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Study objective Triage quickly identifies critically ill patients, facilitating timely interventions. Many emergency departments (EDs) use emergency severity index (ESI) or abnormal vital sign triggers to guide triage. However, both use fixed thresholds, and false activations are costly. Prior approaches using machinelearning have relied on information that is often unavailable during the triage process. We examined whether deep‐learning approaches could identify critically ill patients only using data immediately available at triage. Methods We conducted a retrospective, cross‐sectional study at an urban tertiary care center, from January 1, 2012–January 1, 2020. De‐identified triage information included structured (age, sex, initial vital signs) and textual (chief complaint) data, with critical illness (mortality or ICU admission within 24 hours) as the outcome. Four progressively complex deep‐learning models were trained and applied to triage information from all patients. We compared the accuracy of the models against ESI as the standard diagnostic test, using area under the receiver‐operator curve (AUC). Results A total of 445,925 patients were included, with 60,901 (13.7%) critically ill. Vital sign thresholds identified critically ill patients with AUC 0.521 (95% confidence interval [CI] = 0.519–0.522), and ESI <3 demonstrated AUC 0.672 (95% CI = 0.671–0.674), logistic regression classified patients with AUC 0.803 (95% CI = 0.802–0.804), 2‐layer neural network with structured data with AUC 0.811 (95% CI = 0.807–0.815), gradient tree boosting with AUC 0.820 (95% CI = 0.818–0.821), and the neural network model with textual data with AUC 0.851 (95% CI = 0.849–0.852). All successive increases in AUC were statistically significant. Conclusion Deep‐learning techniques represent a promising method of augmenting triage, even with limited information. Further research is needed to determine if improved predictions yield clinical and operational benefits.

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“…There have been many different measures of resource use and illness severity used across studies evaluating the efficacy of triage and machine-learning predictions, including specific diagnoses such as sepsis (8), admission (7), and overall mortality. (10) The composite of mortality and ICU admission is an appealing compromise metric, as it identifies a population that is more likely to require rapid intervention than the larger population of patients needing admission.…”

Section: Methodsmentioning

confidence: 99%

Deep-Learning Approaches to Identify Critically Ill Patients at Emergency Department Triage Using Limited Information

Joseph

Leventhal

Grossestreuer

et al. 2020

Preprint

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Importance Triage quickly identifies critically ill patients, helping to facilitate timely interventions. Many emergency departments use the emergency severity index (ESI) or abnormal vital sign thresholds to identify critically ill patients. However, both rely on fixed thresholds, and false activations detract from efficient care. Prior research suggests that machine-learning approaches may improve triage accuracy, but have relied on information that is often unavailable during the triage process. Objective We examined whether deep-learning approaches could identify critically ill patients using data immediately available at triage with greater discriminative power than ESI or abnormal vital sign thresholds. Design Retrospective, cross-sectional study. Setting An urban tertiary care hospital in the Northeastern United States. Participants Adult patients presenting to the emergency department from 1/1/2012 - 1/1/2020 were included. Deidentified triage information included structured data (age, sex, initial vital signs, ESI score, and clinical trigger activation due to abnormal vital signs), and textual data (chief complaint) with critical illness (defined as mortality or ICU admission within 24 hours) as the outcome. Interventions Three progressively complex deep-learning models were trained (logistic regression on structured data, neural network on structured data, and neural network on structured and textual data), and applied to triage information from all patients. Main Outcomes and Measures The primary outcome was the accuracy of the model for predicting whether patients were critically ill using area under the receiver-operator curve (AUC), as compared to ESI, utilizing a 10-fold cross-validation. Results 445,925 patients were included, with 60,901 (13.7%) critically ill. Vital sign thresholds identified critically ill patients with AUC 0.521 (95% CI 0.519 -- 0.522), and ESI less than 3 demonstrated AUC 0.672 (95% CI 0.671 -- 0.674), logistic regression classified patients with AUC 0.803 (95% CI 0.802 -- 0.804), neural network with structured data with 0.811 (95% CI 0.807 - 0.815), and the neural network model with textual data with AUC 0.851 (95% CI 0.849 -- 0.852). Conclusions and Relevance Deep-learning techniques represent a promising method of enhancing the triage process, even when working from limited information. Further research is needed to determine if improved predictions can be translated into meaningful clinical and operational benefits.

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Clinical Decision Support Systems for Triage in the Emergency Department using Intelligent Systems: a Review

Cited by 169 publications

References 99 publications

Development and Validation of a Quick Sepsis-Related Organ Failure Assessment-Based Machine-Learning Model for Mortality Prediction in Patients with Suspected Infection in the Emergency Department

Development and Validation of a Quick Sepsis-Related Organ Failure Assessment-Based Machine-Learning Model for Mortality Prediction in Patients with Suspected Infection in the Emergency Department

Deep‐learning approaches to identify critically Ill patients at emergency department triage using limited information

Deep-Learning Approaches to Identify Critically Ill Patients at Emergency Department Triage Using Limited Information

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