Decision support system for triage management: A hybrid approach using rule-based reasoning and fuzzy logic

Soufi, Mahsa Dehghani; Samad‐Soltani, Taha; Rezaei‐Hachesu, Peyman

doi:10.1016/j.ijmedinf.2018.03.008

Cited by 50 publications

(25 citation statements)

References 23 publications

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“…This demand was clearly shown in a survey of triage nurses, where the most requested feature for a new tool being built was an automatic severity grade calculator for the emergency severity index, which is a widely used triage instrument [28]. Due to the inherent complexity and uncertainty involved with the triage of patients, misclassification, over-triage, and under-triage are always possible [27], and a rule-based decision support system for triage has been shown to reduce classification errors [29]. A support tool based on machine learning and NLP may also reduce triage errors and may be more robust to out-of-vocabulary terms than a rule-based system; this would be worth exploring in future studies.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning-Based Prediction of Korean Triage and Acuity Scale Level in Emergency Department Patients

Choi

Hong

et al. 2019

Healthc Inform Res

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ObjectivesTriage is a process to accurately assess and classify symptoms to identify and provide rapid treatment to patients. The Korean Triage and Acuity Scale (KTAS) is used as a triage instrument in all emergency centers. The aim of this study was to train and compare machine learning models to predict KTAS levels.MethodsThis was a cross-sectional study using data from a single emergency department of a tertiary university hospital. Information collected during triage was used in the analysis. Logistic regression, random forest, and XGBoost were used to predict the KTAS level.ResultsThe models with the highest area under the receiver operating characteristic curve (AUROC) were the random forest and XGBoost models trained on the entire dataset (AUROC = 0.922, 95% confidence interval 0.917–0.925 and AUROC = 0.922, 95% confidence interval 0.918–0.925, respectively). The AUROC of the models trained on the clinical data was higher than that of models trained on text data only, but the models trained on all variables had the highest AUROC among similar machine learning models.ConclusionsMachine learning can robustly predict the KTAS level at triage, which may have many possibilities for use, and the addition of text data improves the predictive performance compared to that achieved by using structured data alone.

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Section: Discussionmentioning

confidence: 99%

Machine Learning-Based Prediction of Korean Triage and Acuity Scale Level in Emergency Department Patients

Choi

Hong

et al. 2019

Healthc Inform Res

View full text Add to dashboard Cite

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“…There is a richness of information contained in electronic health records (EHR) stored in large databases that can be explored using machine learning to provide insights to assist providers in making informed decisions based on objective criteria [7]. In the literature machine learning models have been developed to assist in the stratification of patients for prioritization, according to their acuity level at the triage [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23], and according to their risk of mortality [24][25][26][27][28][29][30][31][32][33][34][35][36][37], cardiac arrest [32][33][34], Intensive Care Unit (ICU) admission [27][28][29][30], hospital admission [9,27,[38][39][40][41][42][43]…”

Section: Prior Work In Machine Learning For Risk Stratificationmentioning

confidence: 99%

Risk of mortality and cardiopulmonary arrest in critical patients presenting to the emergency department using machine learning and natural language processing

et al. 2020

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Emergency department triage is the first point in time when a patient's acuity level is determined. The time to assign a priority at triage is short and it is vital to accurately stratify patients at this stage, since under-triage can lead to increased morbidity, mortality and costs. Our aim was to present a model that can assist healthcare professionals in triage decision making, namely in the stratification of patients through the risk prediction of a composite critical outcome-mortality and cardiopulmonary arrest. Our study cohort consisted of 235826 adult patients triaged at a Portuguese Emergency Department from 2012 to 2016. Patients were assigned to emergent, very urgent or urgent priorities of the Manchester Triage System (MTS). Demographics, clinical variables routinely collected at triage and the patients' chief complaint were used. Logistic regression, random forests and extreme gradient boosting were developed using all available variables. The term frequency-inverse document frequency (TF-IDF) natural language processing weighting factor was applied to vectorize the chief complaint. Stratified random sampling was used to split the data into train (70%) and test (30%) data sets. Tenfold cross validation was performed in train to optimize model hyper-parameters. The performance obtained with the best model was compared against the reference model-a regularized logistic regression trained using only triage priorities. Extreme gradient boosting exhibited good calibration properties and yielded areas under the receiver operating characteristic and precision-recall curves of 0.96 (95% CI 0.95-0.97) and 0.31 (95% CI 0.26-0.36), respectively. The predictors ranked with higher importance by this model were the Glasgow coma score, the patients' age, pulse oximetry and arrival mode. Compared to the reference, the extreme gradient boosting model using clinical

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“… [1] , preventive measures and early diagnosis of COVID-19 are crucial to stop the spread of the virus and avoid local outbreaks. Hence, hospital triage is a fundamental step in medical treatment, since, it is the first point of contact with the patient’s symptoms, enabling the classification and prioritization of care [7] , [8] . In triage, health care professionals prioritize patients for urgent care based on an initial clinical assessment [8] , [9] .…”

Section: Introductionmentioning

confidence: 99%

“… [6] , initial clinical assessments in the emergency setting are important in determining the need for further diagnostic and therapeutic steps. Therefore, in emergency settings, rapid and accurate patient triage is a critical first step of the investigation - screening process [8] .…”

Section: Introductionmentioning

confidence: 99%

A fuzzy approach to support decision-making in the triage process for suspected COVID-19 patients in Brazil

Galo

Roriz²,

Pereira³

2022

Applied Soft Computing

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Decision support system for triage management: A hybrid approach using rule-based reasoning and fuzzy logic

Cited by 50 publications

References 23 publications

Machine Learning-Based Prediction of Korean Triage and Acuity Scale Level in Emergency Department Patients

Machine Learning-Based Prediction of Korean Triage and Acuity Scale Level in Emergency Department Patients

Risk of mortality and cardiopulmonary arrest in critical patients presenting to the emergency department using machine learning and natural language processing

A fuzzy approach to support decision-making in the triage process for suspected COVID-19 patients in Brazil

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