Secondary triage classification using an ensemble random forest technique

Azeez, Dhifaf; Beng, Gan Kok; Ali, Mohd Alaudin Mohd; Ismail, Mohd Iswadi

doi:10.3233/thc-150907

Cited by 14 publications

(5 citation statements)

References 35 publications

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“…Ensemble learning methods are classified into: (1) bagging ensemble methods, where base learners are generated simultaneously (i.e., RF), and (2) boosting ensemble methods, in which the base models learn sequentially, using the knowledge of prior models' errors to boost performance (i.e., XGBoost) [29]. Both RF and XGBoost have proven satisfactory performance in previous research and are therefore considered in this paper [30][31][32].…”

Section: Machine Learning Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Forecasting Future Research Trends in the Construction Engineering and Management Domain Using Machine Learning and Social Network Analysis

Ali,

El-adaway,

Ahmed

et al. 2024

Modelling

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Construction Engineering and Management (CEM) is a broad domain with publications covering interrelated subdisciplines and considered a key source of knowledge sharing. Previous studies used scientometric methods to assess the current impact of CEM publications; however, there is a need to predict future citations of CEM publications to identify the expected high-impact trends in the future and guide new research efforts. To tackle this gap in the literature, the authors conducted a study using Machine Learning (ML) algorithms and Social Network Analysis (SNA) to predict CEM-related citation metrics. Using a dataset of 93,868 publications, the authors trained and tested two machine learning classification algorithms: Random Forest and XGBoost. Validation of the RF and XGBoost resulted in a balanced accuracy of 79.1% and 79.5%, respectively. Accordingly, XGBoost was selected. Testing of the XGBoost model revealed a balanced accuracy of 80.71%. Using SNA, it was found that while the top CEM subdisciplines in terms of the number of predicted impactful papers are “Project planning and design”, “Organizational issues”, and “Information technologies, robotics, and automation”; the lowest was “Legal and contractual issues”. This paper contributes to the body of knowledge by studying the citation level, strength, and interconnectivity between CEM subdisciplines as well as identifying areas more likely to result in highly cited publications.

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Section: Machine Learning Modelsmentioning

confidence: 99%

“…RF models are more generalizable and less prone to overfitting [36]. As such, RF models are recognized for their increased classification performance and improved prediction accuracy [30].…”

Section: Rf Classifiermentioning

confidence: 99%

Forecasting Future Research Trends in the Construction Engineering and Management Domain Using Machine Learning and Social Network Analysis

Ali,

El-adaway,

Ahmed

et al. 2024

Modelling

View full text Add to dashboard Cite

show abstract

“…In recent times, machine and deep learning algorithms, including decision tree, Support Vector Machines (SVM), logistic regression, Naïve Bayes, Artificial Neural Networks (ANN) and others, have been extensively applied to medical triaging referrals research [ 9 ]. For example, an ensemble random forest technique was employed to triage patients in the emergency department in order to avoid potential fatality and increased waiting time [ 10 ]. Triage prediction models have been developed using SVM coupled with Principal Component Analysis (PCA) to effectively predict anomaly detection and triage [ 11 ].…”

Section: Related Workmentioning

confidence: 99%

Triaging Medical Referrals Based on Clinical Prioritisation Criteria Using Machine Learning Techniques

Wee

Zhou

Sun

et al. 2022

IJERPH

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Triaging of medical referrals can be completed using various machine learning techniques, but trained models with historical datasets may not be relevant as the clinical criteria for triaging are regularly updated and changed. This paper proposes the use of machine learning techniques coupled with the clinical prioritisation criteria (CPC) of Queensland (QLD), Australia, to deliver better triaging for referrals in accordance with the CPC’s updates. The unique feature of the proposed model is its non-reliance on the past datasets for model training. Medical Natural Language Processing (NLP) was applied in the proposed approach to process the medical referrals, which are unstructured free text. The proposed multiclass classification approach achieved a Micro F1 score = 0.98. The proposed approach can help in the processing of two million referrals that the QLD health service receives annually; therefore, they can deliver better and more efficient health services.

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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

show abstract

Secondary triage classification using an ensemble random forest technique

Abstract: With this combination, the random forest reduces the variance, and the randomized resembling reduces the bias, leading to the reduced out-of-bag error.

Cited by 14 publications

References 35 publications

Forecasting Future Research Trends in the Construction Engineering and Management Domain Using Machine Learning and Social Network Analysis

Forecasting Future Research Trends in the Construction Engineering and Management Domain Using Machine Learning and Social Network Analysis

Triaging Medical Referrals Based on Clinical Prioritisation Criteria Using Machine Learning Techniques

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

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