A Machine Learning Model for Triage in Lean Pediatric Emergency Departments

Caicedo-Torres, William; Garcia, Gisela; Pinzón, Hernando

doi:10.1007/978-3-319-47955-2_18

Cited by 3 publications

(5 citation statements)

References 16 publications

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“…They used logistic regression, support vector machines and neural networks for comparison. They did not report discrimination and only reported limited classification statistics [26]. They found that the neural network had the most precise estimates with a PPV (0.85) compared to the support vector machine and logistic regression (0.83 and 0.82).…”

Section: Discharge Outcomementioning

confidence: 99%

“…This limits the benefit of grouping high vs low risk of bias studies. Most studies had low applicability concern, except for six studies [26,30,38,41,46,49].…”

Section: Risk Of Bias and Applicabilitymentioning

confidence: 99%

“…Three models from a single study used discharge related outcome measures [26]. The study focussed on predicting patients that would be discharged from the ED, and diverting them to a fast track service.…”

Section: Discharge Outcomementioning

confidence: 99%

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Using machine-learning risk prediction models to triage the acuity of undifferentiated patients entering the emergency care system: a systematic review

Miles

Turner²,

Jacques³

et al. 2020

Diagn Progn Res

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Background The primary objective of this review is to assess the accuracy of machine learning methods in their application of triaging the acuity of patients presenting in the Emergency Care System (ECS). The population are patients that have contacted the ambulance service or turned up at the Emergency Department. The index test is a machine-learning algorithm that aims to stratify the acuity of incoming patients at initial triage. This is in comparison to either an existing decision support tool, clinical opinion or in the absence of these, no comparator. The outcome of this review is the calibration, discrimination and classification statistics. Methods Only derivation studies (with or without internal validation) were included. MEDLINE, CINAHL, PubMed and the grey literature were searched on the 14th December 2019. Risk of bias was assessed using the PROBAST tool and data was extracted using the CHARMS checklist. Discrimination (C-statistic) was a commonly reported model performance measure and therefore these statistics were represented as a range within each machine learning method. The majority of studies had poorly reported outcomes and thus a narrative synthesis of results was performed. Results There was a total of 92 models (from 25 studies) included in the review. There were two main triage outcomes: hospitalisation (56 models), and critical care need (25 models). For hospitalisation, neural networks and tree-based methods both had a median C-statistic of 0.81 (IQR 0.80-0.84, 0.79-0.82). Logistic regression had a median C-statistic of 0.80 (0.74-0.83). For critical care need, neural networks had a median C-statistic of 0.89 (0.86-0.91), tree based 0.85 (0.84-0.88), and logistic regression 0.83 (0.79-0.84). Conclusions Machine-learning methods appear accurate in triaging undifferentiated patients entering the Emergency Care System. There was no clear benefit of using one technique over another; however, models derived by logistic regression were more transparent in reporting model performance. Future studies should adhere to reporting guidelines and use these at the protocol design stage. Registration and funding This systematic review is registered on the International prospective register of systematic reviews (PROSPERO) and can be accessed online at the following URL: https://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42020168696 This study was funded by the NIHR as part of a Clinical Doctoral Research Fellowship.

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Section: Discharge Outcomementioning

confidence: 99%

“…This limits the benefit of grouping high vs low risk of bias studies. Most studies had low applicability concern, except for six studies [26,30,38,41,46,49].…”

Section: Risk Of Bias and Applicabilitymentioning

confidence: 99%

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Using machine-learning risk prediction models to triage the acuity of undifferentiated patients entering the emergency care system: a systematic review

Miles

Turner²,

Jacques³

et al. 2020

Diagn Progn Res

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“…Machine learning is increasingly used in healthcare informatics [21], also in the case of patient referral. Recent examples are systems in emergency departments to identify patients with suspected infection [22] and to identify low-complexity patients that can be included in a separate fast track patient stream to save waiting time and capacity [23]. In case of musculoskeletal problems, the Work Assessment Triage Tool (WATT) is an example of a machine learned CDSS that refers workers with musculoskeletal injuries to optimal rehabilitation interventions [24].…”

mentioning

confidence: 99%

Evaluation of three machine learning models for self-referral decision support on low back pain in primary care

Nijeweme-d’Hollosy

Velsen

Poel

et al. 2018

International Journal of Medical Informatics

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“…There are few more works which predict the patient’s clinical deterioration using machine learning algorithms (Sakanushi et al , 2012; Caicedo-Torres et al , 2012; Claudio et al , 2014; Elalouf and Wachtel, 2016; Salman et al , 2017; Salem et al , 2014; Seera et al , 2015; Brisimi et al , 2018; Taylor et al , 2016; Henriques et al , 2015; Zufferey et al , 2015; Olivia et al , 2018) at ED. None of them have focused on time series data and usage of various medical scores to predict the clinical severity level in advance.…”

Section: Literature Reviewmentioning

confidence: 99%

A classification model for prediction of clinical severity level using qSOFA medical score

Olivia

Nayak

Balachandra

et al. 2020

IDD

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Purpose The purpose of this study is to develop an efficient prediction model using vital signs and standard medical score systems, which predicts the clinical severity level of the patient in advance based on the quick sequential organ failure assessment (qSOFA) medical score method. Design/methodology/approach To predict the clinical severity level of the patient in advance, the authors have formulated a training dataset that is constructed based on the qSOFA medical score method. Further, along with the multiple vital signs, different standard medical scores and their correlation features are used to build and improve the accuracy of the prediction model. It is made sure that the constructed training set is suitable for the severity level prediction because the formulated dataset has different clusters each corresponding to different severity levels according to qSOFA score. Findings From the experimental result, it is found that the inclusion of the standard medical scores and their correlation along with multiple vital signs improves the accuracy of the clinical severity level prediction model. In addition, the authors showed that the training dataset formulated from the temporal data (which includes vital signs and medical scores) based on the qSOFA medical scoring system has the clusters which correspond to each severity level in qSOFA score. Finally, it is found that RAndom k-labELsets multi-label classification performs better prediction of severity level compared to neural network-based multi-label classification. Originality/value This paper helps in identifying patient' clinical status.

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A Machine Learning Model for Triage in Lean Pediatric Emergency Departments

Cited by 3 publications

References 16 publications

Using machine-learning risk prediction models to triage the acuity of undifferentiated patients entering the emergency care system: a systematic review

Using machine-learning risk prediction models to triage the acuity of undifferentiated patients entering the emergency care system: a systematic review

Evaluation of three machine learning models for self-referral decision support on low back pain in primary care

A classification model for prediction of clinical severity level using qSOFA medical score

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