Predicting hospital admission at emergency department triage using machine learning

Hong, Woo Suk; Haimovich, Adrian Daniel; Taylor, R. Andrew

doi:10.1371/journal.pone.0201016

Cited by 326Machine Learning in HealthcareEmergency and Acute Care StudiesArtificial Intelligence in Healthcare

Predicting hospital admission at emergency department triage using machine learning

Open access

Hong, Woo Suk, Haimovich, Adrian Daniel, Taylor, R. Andrew·2018·PLOS ONE

DOI 10.1371/journal.pone.0201016PMC PMC6054406

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Abstract

OBJECTIVE: To predict hospital admission at the time of ED triage using patient history in addition to information collected at triage. METHODS: This retrospective study included all adult ED visits between March 2014 and July 2017 from one academic and two community emergency rooms that resulted in either admission or discharge. A total of 972 variables were extracted per patient visit. Samples were randomly partitioned into training (80%), validation (10%), and test (10%) sets. We trained a series of nine binary classifiers using logistic regression (LR), gradient boosting (XGBoost), and deep neural networks (DNN) on three dataset types: one using only triage information, one using only patient history, and one using the full set of variables. Next, we tested the potential benefit of additional training samples by training models on increasing fractions of our data. Lastly, variables of importance were identified using information gain as a metric to create a low-dimensional model. RESULTS: A total of 560,486 patient visits were included in the study, with an overall admission risk of 29.7%. Models trained on triage information yielded a test AUC of 0.87 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.88) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on patient history yielded an AUC of 0.86 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.87) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on the full set of variables yielded an AUC of 0.91 for LR (95% CI 0.91-0.91), 0.92 for XGBoost (95% CI 0.92-0.93) and 0.92 for DNN (95% CI 0.92-0.92). All algorithms reached maximum performance at 50% of the training set or less. A low-dimensional XGBoost model built on ESI level, outpatient medication counts, demographics, and hospital usage statistics yielded an AUC of 0.91 (95% CI 0.91-0.91). CONCLUSION: Machine learning can robustly predict hospital admission using triage information and patient history. The addition of historical information improves predictive performance significantly compared to using triage information alone, highlighting the need to incorporate these variables into prediction models.

AI snapshot

Written from the paper’s full text.

TL;DR

Machine learning models using electronic health record data predicted hospital admission at ED triage, with full-variable models reaching test AUCs of 0.91–0.92. Adding patient history significantly improved performance over triage information alone.

Key findings

Among 560,486 adult ED visits with an overall admission risk of 29.7%, full-variable models achieved test AUC 0.91 for logistic regression, 0.92 for XGBoost, and 0.92 for deep neural networks; triage-only and history-only models achieved AUCs around 0.86–0.87.
A low-dimensional XGBoost model using ESI level, outpatient medication counts, demographics, and hospital usage statistics yielded an AUC of 0.91 (95% CI 0.91–0.91).
All algorithms reached maximum performance at 50% of the training set or less; logistic regression reached it at 10%, while XGBoost and DNN reached it at 50%.
XGBoost and DNN outperformed logistic regression on the full dataset, with no significant difference between XGBoost and DNN across the three dataset types.

Methods

Retrospective study of all adult ED visits between March 2014 and July 2017 from one academic and two community EDs that ended in admission or discharge; 972 variables were extracted per visit, and 560,486 visits were randomly partitioned into training (80%), validation (10%), and test (10%) sets.
Nine binary classifiers were trained using logistic regression, XGBoost, and deep neural networks on three dataset types: triage information only, patient history only, and the full variable set; hyperparameters were optimized by average validation AUC across five validation sets, and test AUCs were calculated with DeLong 95% CIs.
Additional analyses trained full-variable models on increasing fractions of the training set (1%, 10%, 30%, 50%, 80%, 100%) and used information gain from 100 XGBoost iterations to identify important variables and build a low-dimensional model.

Limitations

The paper states that patient history was restricted to previous ED visits, and expanding to outpatient or inpatient data may improve performance but could present EHR integration and differing-standards difficulties.
The paper states that data came from one hospital system with multiple EDs and a large catchment area, and extending the approach to dense urban areas with multiple independent EDs may be difficult because patients may not consistently present to the same system.
The paper states that the outcome label was the ED provider’s prior disposition decision, so appropriateness of individual clinical decisions could not be assessed, and it does not address implementation or efficacy barriers in clinical practice; evident from the text, the study is retrospective, adult-only, excludes non-admission/discharge dispositions, and uses a held-out test set from the same health system rather than external validation.

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Crossref · 10.1371/journal.pone.0201016

Cites

57 of 57 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 46 cited sources checked · 0 supported · 0 flagged

Findingown data
Models trained on triage information yielded a test AUC of 0.87 for DNN (95% CI 0.87-0.88).
“Models trained on triage information yielded a test AUC of 0.87 for LR (95% CI 0.86–0.87), 0.87 for XGBoost (95% CI 0.87–0.88) and 0.87 for DNN (95% CI 0.87–0.88).”
Quote in the paperSubstantial claim
Findingown data
The addition of patient history improves predictive performance significantly compared to using triage information alone.
“the addition of patient history improves predictive performance significantly compared to using triage information alone”
Quote in the paperSubstantial claim
Findingown data
Prior hospital usage statistics such as the number of admissions within the past year and the disposition of the previous ED visit had high information gain in the full XGBoost model.
“as did prior hospital usage statistics such as the number of admissions within the past year and the disposition of the previous ED visit.”
Quote in the paperNot a substantial claim
Findingown data
For LR, the 95% CI of the AUC of the model trained on 10% of the training set contained the AUC of the model trained on the entire training set.
“For LR, the 95% CI of the AUC of the model trained on 10% of the training set contained the AUC of the model trained on the entire training set.”
Quote in the paperSubstantial claim
Findingown data
Models trained on patient history yielded an AUC of 0.86 for LR (95% CI 0.86-0.87).
“Models trained on patient history yielded an AUC of 0.86 for LR (95% CI 0.86–0.87), 0.87 for XGBoost (95% CI 0.87–0.87) and 0.87 for DNN (95% CI 0.87–0.88).”
Quote in the paperNot a substantial claim

Findingown data
Models trained on the full set of variables yielded an AUC of 0.92 for DNN (95% CI 0.92-0.92).
“Models trained on the full set of variables yielded an AUC of 0.91 for LR (95% CI 0.91–0.91), 0.92 for XGBoost (95% CI 0.92–0.93) and 0.92 for DNN (95% CI 0.92–0.92).”
Quote in the paperSubstantial claim
Findingown data
Variables correlated with age and markers of socioeconomic status such as insurance type were some of the other features identified by the model.
“Variables correlated with age and markers of socioeconomic status such as insurance type were some of the other features identified by our model”
Quote in the paperNot a substantial claim
Findingnone given
All algorithms reached maximum performance at 50% of the training set or less.
“All algorithms reached maximum performance at 50% of the training set or less.”
Quote in the paperNot a substantial claim
Findingown data
There was no significant difference in performance between XGBoost and DNN across all three dataset types.
“XGBoost and DNN outperformed LR on the full dataset, while there was no significant difference in performance between XGBoost and DNN across all three dataset types.”
Quote in the paperNot a substantial claim
Findingown data
Models trained on the full set of variables yielded an AUC of 0.91 for LR (95% CI 0.91-0.91).
“Models trained on the full set of variables yielded an AUC of 0.91 for LR (95% CI 0.91–0.91), 0.92 for XGBoost (95% CI 0.92–0.93) and 0.92 for DNN (95% CI 0.92–0.92).”
Quote in the paperSubstantial claim
Findingown data
XGBoost and DNN outperformed LR on the full dataset.
“XGBoost and DNN outperformed LR on the full dataset, while there was no significant difference in performance between XGBoost and DNN across all three dataset types.”
Quote in the paperNot a substantial claim
Findingown data
Models trained on patient history yielded an AUC of 0.87 for DNN (95% CI 0.87-0.88).
“Models trained on patient history yielded an AUC of 0.86 for LR (95% CI 0.86–0.87), 0.87 for XGBoost (95% CI 0.87–0.87) and 0.87 for DNN (95% CI 0.87–0.88).”
Quote in the paperSubstantial claim
Findingown data
The triage level encoded by the Emergency Severity Index (ESI) had high information gain in the full XGBoost model.
“Not surprisingly, the triage level encoded by the Emergency Severity Index (ESI) had high information gain [44,45]”
Quote in the paperNot a substantial claim0 of 2 sources checked
- cites [44] P Tanabe, 2004, Reliability and validity of scores on The Emergency Severity Index version 3, Acad Emerg Med source
  Not checked in the free run.
- cites [45] MR Baumann, 2007, Triage of geriatric patients in the emergency department: validity and survival with the Emergency Severity Index, Ann Emerg Med source
  Not checked in the free run.
Findingown data
Variables representing ESI level, outpatient medication counts, demographics, and hospital usage statistics showed high information gain in the full XGBoost model.
“Variables representing ESI level, outpatient medication counts, demographics, and hospital usage statistics showed high information gain.”
Quote in the paperSubstantial claim
Findingown data
Models trained on triage information yielded a test AUC of 0.87 for LR (95% CI 0.86-0.87).
“Models trained on triage information yielded a test AUC of 0.87 for LR (95% CI 0.86–0.87), 0.87 for XGBoost (95% CI 0.87–0.88) and 0.87 for DNN (95% CI 0.87–0.88).”
Quote in the paperSubstantial claim
Findingown data
For XGBoost and DNN, the point at which the 95% CI of the AUC of the model trained on a fraction of the training set contained the AUC of the model trained on the entire training set was at 50% of the training set.
“For XGBoost and DNN, the point at which this occurred was at 50% of the training set (Fig 2).”
Quote in the paperNot a substantial claim
Findingown data
A low-dimensional XGBoost model built on ESI level, outpatient medication counts, demographics, and hospital usage statistics yielded an AUC of 0.91 (95% CI 0.91-0.91).
“A low-dimensional XGBoost model built on ESI level, outpatient medication counts, demographics, and hospital usage statistics yielded an AUC of 0.91 (95% CI 0.91–0.91).”
Quote in the paperSubstantial claim
Findingown data
A total of 560,486 patient visits were included in the study.
“A total of 560,486 patient visits were included in the study, with an overall admission risk of 29.7%.”
- Also stated in: § Characteristics of study samples · plain
  “A total of 560,486 ED visits were available for analysis after filtering for exclusion criteria, with 13% of the samples excluded due to disposition other than admission or discharge.”
Quote in the paperSubstantial claim
Findingown data
The overall hospital admission risk was 29.7%.
“A total of 560,486 patient visits were included in the study, with an overall admission risk of 29.7%.”
- Also stated in: § Characteristics of study samples · plain
  “The overall hospital admission risk was 29.7% and decreased by triage level: ESI-1 85.6%, ESI-2 55.0%, ESI-3 29.1%, ESI-4 2.2%, and ESI-5 0.4% (S1 Fig).”
Quote in the paperNot a substantial claim
Findingown data
Models trained on patient history yielded an AUC of 0.87 for XGBoost (95% CI 0.87-0.87).
“Models trained on patient history yielded an AUC of 0.86 for LR (95% CI 0.86–0.87), 0.87 for XGBoost (95% CI 0.87–0.87) and 0.87 for DNN (95% CI 0.87–0.88).”
Quote in the paperSubstantial claim
Findingown data
Using patient history alone, excluding triage level, achieved an AUC of over 0.86.
“Notably, we were able to achieve an AUC of over 0.86 by using patient history alone, which excludes triage level.”
Quote in the paperSubstantial claim
Findingown data
The addition of historical information improves predictive performance significantly compared to using triage information alone.
“The addition of historical information improves predictive performance significantly compared to using triage information alone, highlighting the need to incorporate these variables into prediction models.”
- Also stated in: § Model performance · plain
  “The addition of historical information improved predictive performance significantly compared to using triage information alone (Fig 1).”
Quote in the paperSubstantial claim
Findingown data
LR reaches maximum performance earlier than XGBoost or DNN.
“LR reaches maximum performance earlier than XGBoost or DNN.”
Quote in the paperNot a substantial claim
Findingown data
Models trained on the full set of variables yielded an AUC of 0.92 for XGBoost (95% CI 0.92-0.93).
“Models trained on the full set of variables yielded an AUC of 0.91 for LR (95% CI 0.91–0.91), 0.92 for XGBoost (95% CI 0.92–0.93) and 0.92 for DNN (95% CI 0.92–0.92).”
Quote in the paperSubstantial claim
Findingown data
Models trained on triage information yielded a test AUC of 0.87 for XGBoost (95% CI 0.87-0.88).
“Models trained on triage information yielded a test AUC of 0.87 for LR (95% CI 0.86–0.87), 0.87 for XGBoost (95% CI 0.87–0.88) and 0.87 for DNN (95% CI 0.87–0.88).”
Quote in the paperSubstantial claim
Findingown data
The full XGBoost model achieved a test AUC of 0.924 (95% CI 0.922-0.927).
“| XGBoost | Full | 0.924 (0.922–0.927) | 0.83 | 0.85 | 0.70 | 0.92 |”
Quote in the paperSubstantial claim
Findingown data
The full LR model achieved a test AUC of 0.909 (95% CI 0.906-0.911).
“| LR | Full | 0.909 (0.906–0.911) | 0.80 | 0.85 | 0.69 | 0.91 |”
Quote in the paperSubstantial claim
Findingown data
The full DNN model achieved a test AUC of 0.920 (95% CI 0.917-0.922).
“| DNN | Full | 0.920 (0.917–0.922) | 0.82 | 0.85 | 0.70 | 0.92 |”
Quote in the paperSubstantial claim
Interpretationown data
Machine learning can robustly predict hospital admission using triage information and patient history.
“Machine learning can robustly predict hospital admission using triage information and patient history.”
Quote in the paperSubstantial claim
Interpretationown data
Machine learning can robustly predict hospital admission at emergency department triage.
“Our study shows that machine learning can robustly predict hospital admission at emergency department (ED) triage”
Quote in the paperSubstantial claim
- Rests on an assumption: The variables used in the models are available at the time of emergency department triage.
Interpretationown data
The plateau in AUC with increasing training set fractions suggests that the discriminatory capability for the feature set has likely been maximized.
“This result suggests that we have likely maximized the discriminatory capability for our feature set.”
Quote in the paperSubstantial claim
Premisecitation
Most prior models predicting hospital admission at ED triage include only information collected at triage such as demographics, vital signs, chief complaint, nursing notes, and early diagnostics.
“Most models only include information collected at triage such as demographics, vital signs, chief complaint, nursing notes, and early diagnostics [11,14–19]”
Quote in the paperSubstantial claim0 of 7 sources checked
- cites [11] JS Peck, 2012, Predicting emergency department inpatient admissions to improve same-day patient flow, Acad Emerg Med source
  Not checked in the free run.
- cites [14] AF Dugas, 2016, An Electronic Emergency Triage System to Improve Patient Distribution by Critical Outcomes, J Emerg Med source
  Not checked in the free run.
- cites [15] J Leegon, 2006, Predicting Hospital Admission in a Pediatric Emergency Department using an Artificial Neural Network, AMIA Annu Symp Proc
  Not checked in the free run.
- cites [16] J Leegon, 2005, Predicting Hospital Admission for Emergency Department Patients using a Bayesian Network, AMIA Annu Symp Proc
  Not checked in the free run.
- cites [17] FR Lucini, 2017, Text mining approach to predict hospital admissions using early medical records from the emergency department, Int J Med Inf source
  Not checked in the free run.
- cites [18] DW Savage, 2017, P112: Predicting patient admission from the emergency department using triage administrative data, Can J Emerg Med source
  Not checked in the free run.
- cites [19] JA Lucke, 2017, Early prediction of hospital admission for emergency department patients: a comparison between patients younger or older than 70 years, Emerg Med J
  Not checked in the free run.
Premisecitation
A patient's likelihood of admission may serve as a proxy for acuity, which is used in downstream decisions such as bed placement and the need for emergency intervention.
“From the perspective of patient care in the ED setting, a patient's likelihood of admission may serve as a proxy for acuity, which is used in a number of downstream decisions such as bed placement and the need for emergency intervention [12–14].”
Quote in the paperSubstantial claim0 of 3 sources checked
- cites [12] S Levin, 2017, Machine-Learning-Based Electronic Triage More Accurately Differentiates Patients With Respect to Clinical Outcomes Compared With the Emergency Severity Index, Ann Emerg Med
  Not checked in the free run.
- cites [13] R Arya, 2013, Decreasing length of stay in the emergency department with a split emergency severity index 3 patient flow model, Acad Emerg Med Off J Soc Acad Emerg Med source
  Not checked in the free run.
- cites [14] AF Dugas, 2016, An Electronic Emergency Triage System to Improve Patient Distribution by Critical Outcomes, J Emerg Med source
  Not checked in the free run.
Premisecitation
Many prior studies have been limited by technical factors, where continuous variables are often reduced to categorical variables through binning or to binary variables encoding presence or missing-ness of data due to the challenges of imputation.
“Furthermore, many prior studies have been limited by technical factors, where continuous variables are often reduced to categorical variables through binning or to binary variables encoding presence or missing-ness of data due to the challenges of imputation [9,15,16,19–21].”
Quote in the paperSubstantial claim0 of 6 sources checked
- cites [9] Y Sun, 2011, Predicting hospital admissions at emergency department triage using routine administrative data, Acad Emerg Med source
  Not checked in the free run.
- cites [15] J Leegon, 2006, Predicting Hospital Admission in a Pediatric Emergency Department using an Artificial Neural Network, AMIA Annu Symp Proc
  Not checked in the free run.
- cites [16] J Leegon, 2005, Predicting Hospital Admission for Emergency Department Patients using a Bayesian Network, AMIA Annu Symp Proc
  Not checked in the free run.
- cites [19] JA Lucke, 2017, Early prediction of hospital admission for emergency department patients: a comparison between patients younger or older than 70 years, Emerg Med J
  Not checked in the free run.
- cites [20] Y Barak-Corren, 2017, Progressive prediction of hospitalisation in the emergency department: uncovering hidden patterns to improve patient flow, Emerg Med J source
  Not checked in the free run.
- cites [21] Y Barak-Corren, 2017, Early Prediction Model of Patient Hospitalization From the Pediatric Emergency Department, Pediatrics source
  Not checked in the free run.
Interpretationown data
Patient history alone can predict admission to a reasonable degree.
“Patient history alone can predict admission to a reasonable degree.”
Quote in the paperSubstantial claim
Interpretationown data
These features can be combined to create a low-dimensional model amenable to implementation in EHR systems as clinical decision support.
“We show that these features can be combined to create a low-dimensional model amenable to implementation in EHR systems as clinical decision support.”
Quote in the paperSubstantial claim
Study design and conductreasoning
The study is unable to address the appropriateness of individual clinical decisions because it uses the ED provider's prior decision as the true label.
“In doing so, we are unable to address the appropriateness of individual clinical decisions.”
Quote in the paperSubstantial claim
- Rests on an assumption: The ED provider's prior decision is a valid ground truth for hospital admission.

May combine two claims. 5
The quote only partly states this claim. 1
Classified as probably not a substantial claim. 10

May combine two claims. The quote only partly states this claim.
Findingown data
All three algorithms reach maximum performance at 50% of the training set or less (the AUC plateaus well below the full training set).
“All three algorithms reach maximum performance at 50% of the training set or less.”
- Also stated in: § Discussion · boosted
  “we train our models on increasing fractions of the training set and show that the AUC plateaus well below the full training set.”
Quote in the paperSubstantial claim
Probably not a substantial claim: may not pass S3 (load-bearing).
Premisecitation
EDs represent the largest source of hospital admissions.
“While most emergency department (ED) visits end in discharge, EDs represent the largest source of hospital admissions [1].”
Quote in the paperNot a substantial claim0 of 1 sources checked
- cites [1] Moore B, Stocks C, Owens P. Trends in Emergency Department Visits, 2006–2014. HCUP Statistical Brief #227 [Internet]. Agency for Healthcare Research and Quality; 2017 Sep. Available: <ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://www.hcup-us.ahrq.gov/reports/statbriefs/sb227-Emergency-Department-Visit-Trends.pdf" xlink:type="simple">https://www.hcup-us.ahrq.gov/reports/statbriefs/sb227-Emergency-Department-Visit-Trends.pdf</ext-link>
  Not checked in the free run.
Classified as probably not a substantial claim.
Premisecitation
ED crowding is correlated with poorer patient outcomes.
“It is increasingly understood that ED crowding is correlated with poorer patient outcomes [10].”
Quote in the paperSubstantial claim0 of 1 sources checked
- cites [10] SL Bernstein, 2009, The Effect of Emergency Department Crowding on Clinically Oriented Outcomes, Acad Emerg Med source
  Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Interpretationcitation
The importance of medication counts in the model may either reflect a proxy feature for medical complexity or indicate that polypharmacy itself is a risk factor.
“the importance of medication counts in the model may either reflect a proxy feature for medical complexity or indicate that polypharmacy itself is a risk factor [42,43].”
Quote in the paperNot a substantial claim0 of 2 sources checked
- cites [42] CM Hohl, 2001, Polypharmacy, adverse drug-related events, and potential adverse drug interactions in elderly patients presenting to an emergency department, Ann Emerg Med source
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- cites [43] A Banerjee, 2011, The prevalence of polypharmacy in elderly attenders to an emergency department—a problem with a need for an effective solution, Int J Emerg Med source
  Not checked in the free run.
Classified as probably not a substantial claim. May combine two claims.
Premisecitation
A progressive modeling approach that uses information available at later time-points has been able to achieve high predictive power and indicates the utility of these features.
“Notably, a progressive modeling approach that uses information available at later time-points, such as lab tests ordered, medications given, and diagnoses entered by the ED provider during the patient’s current visit, has been able to achieve high predictive power and indicates the utility of these features [20,21].”
Quote in the paperSubstantial claim0 of 2 sources checked
- cites [20] Y Barak-Corren, 2017, Progressive prediction of hospitalisation in the emergency department: uncovering hidden patterns to improve patient flow, Emerg Med J source
  Not checked in the free run.
- cites [21] Y Barak-Corren, 2017, Early Prediction Model of Patient Hospitalization From the Pediatric Emergency Department, Pediatrics source
  Not checked in the free run.
Classified as probably not a substantial claim.
Study design and conductown data
Samples were randomly partitioned into training (80%), validation (10%), and test (10%) sets.
“Samples were randomly partitioned into training (80%), validation (10%), and test (10%) sets.”
- Also stated in: § Model fitting and evaluation · plain
  “A randomly chosen test set of 56,000 (10%) samples was held out, then the remaining 504,486 (90%) samples split randomly five times to create five independent validation sets of 56,000 (10%) and training sets of 448,486 (80%).”
Quote in the paperSubstantial claim
Probably not a substantial claim: may not pass S3 (load-bearing).
Premisecitation
Variables correlated with age and markers of socioeconomic status such as insurance type have been previously linked to hospital admission.
“that have been previously linked to hospital admission [46–48].”
Quote in the paperNot a substantial claim0 of 3 sources checked
- cites [46] AW Selassie, 2003, The Influence of Insurance, Race, and Gender on Emergency Department Disposition, Acad Emerg Med source
  Not checked in the free run.
- cites [47] Jiang HJ, Russo CA, Barrett ML. Nationwide Frequency and Costs of Potentially Preventable Hospitalizations, 2006. HCUP Statistical Brief #72 [Internet]. Agency for Healthcare Research and Quality; 2009 Apr. Available: <ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="http://www.hcup-us.ahrq.gov/reports/statbriefs/sb72.pdf" xlink:type="simple">http://www.hcup-us.ahrq.gov/reports/statbriefs/sb72.pdf</ext-link>
  Not checked in the free run.
- cites [48] S Kangovi, 2013, Understanding Why Patients Of Low Socioeconomic Status Prefer Hospitals Over Ambulatory Care, Health Aff (Millwood) source
  Not checked in the free run.
Classified as probably not a substantial claim.
Premisecitation
Using all elements of the electronic health record can robustly predict in-patient outcomes.
“As a recent work showed that using all elements of the electronic health record can robustly predict in-patient outcomes [26]”
Quote in the paperSubstantial claim0 of 1 sources checked
- cites [26] A Rajkomar, 2018, Scalable and accurate deep learning for electronic health records, Npj Digit Med source
  Not checked in the free run.
May combine two claims.
Premisecitation
Logistic regression and Naive Bayes are commonly used, with few studies using more complex algorithms like random-forests, artificial neural networks, and support vector machines.
“Logistic regression and Naive Bayes are commonly used [9,11,16,18–22], with few studies using more complex algorithms like random-forests, artificial neural networks, and support vector machines [12,15,17].”
Quote in the paperSubstantial claim0 of 10 sources checked
- cites [9] Y Sun, 2011, Predicting hospital admissions at emergency department triage using routine administrative data, Acad Emerg Med source
  Not checked in the free run.
- cites [11] JS Peck, 2012, Predicting emergency department inpatient admissions to improve same-day patient flow, Acad Emerg Med source
  Not checked in the free run.
- cites [16] J Leegon, 2005, Predicting Hospital Admission for Emergency Department Patients using a Bayesian Network, AMIA Annu Symp Proc
  Not checked in the free run.
- cites [18] DW Savage, 2017, P112: Predicting patient admission from the emergency department using triage administrative data, Can J Emerg Med source
  Not checked in the free run.
- cites [19] JA Lucke, 2017, Early prediction of hospital admission for emergency department patients: a comparison between patients younger or older than 70 years, Emerg Med J
  Not checked in the free run.
- cites [20] Y Barak-Corren, 2017, Progressive prediction of hospitalisation in the emergency department: uncovering hidden patterns to improve patient flow, Emerg Med J source
  Not checked in the free run.
- cites [21] Y Barak-Corren, 2017, Early Prediction Model of Patient Hospitalization From the Pediatric Emergency Department, Pediatrics source
  Not checked in the free run.
- cites [22] MM Dinh, 2016, The Sydney Triage to Admission Risk Tool (START) to predict Emergency Department Disposition: A derivation and internal validation study using retrospective state-wide data from New South Wales, Australia, BMC Emerg Med source
  Not checked in the free run.
- cites [12] S Levin, 2017, Machine-Learning-Based Electronic Triage More Accurately Differentiates Patients With Respect to Clinical Outcomes Compared With the Emergency Severity Index, Ann Emerg Med
  Not checked in the free run.
- cites [15] J Leegon, 2006, Predicting Hospital Admission in a Pediatric Emergency Department using an Artificial Neural Network, AMIA Annu Symp Proc
  Not checked in the free run.
May combine two claims.
Premisecitation
Prior models that incorporate past medical history utilize simplified chronic disease categories such as heart disease or diabetes while leaving out rich historical information accessible from the EHR such as outpatient medications and historical labs and vitals.
“Prior models that incorporate past medical history utilize simplified chronic disease categories such as heart disease or diabetes [9,12] while leaving out rich historical information accessible from the electronic health record (EHR) such as outpatient medications and historical labs and vitals”
Quote in the paperSubstantial claim0 of 2 sources checked
- cites [9] Y Sun, 2011, Predicting hospital admissions at emergency department triage using routine administrative data, Acad Emerg Med source
  Not checked in the free run.
- cites [12] S Levin, 2017, Machine-Learning-Based Electronic Triage More Accurately Differentiates Patients With Respect to Clinical Outcomes Compared With the Emergency Severity Index, Ann Emerg Med
  Not checked in the free run.
Probably not a substantial claim: may not pass S2 (checkable).
Premisecitation
Notification of administrators and inpatient teams regarding potential admissions may help alleviate ED crowding.
“Notification of administrators and inpatient teams regarding potential admissions may help alleviate this problem [11].”
Quote in the paperNot a substantial claim0 of 1 sources checked
- cites [11] JS Peck, 2012, Predicting emergency department inpatient admissions to improve same-day patient flow, Acad Emerg Med source
  Not checked in the free run.
Classified as probably not a substantial claim.
Premisecitation
Some prior models predicting hospital admission at ED triage include additional features such as hospital usage statistics and past medical history.
“while some models include additional features such as hospital usage statistics and past medical history [9,12,20,21].”
Quote in the paperSubstantial claim0 of 4 sources checked
- cites [9] Y Sun, 2011, Predicting hospital admissions at emergency department triage using routine administrative data, Acad Emerg Med source
  Not checked in the free run.
- cites [12] S Levin, 2017, Machine-Learning-Based Electronic Triage More Accurately Differentiates Patients With Respect to Clinical Outcomes Compared With the Emergency Severity Index, Ann Emerg Med
  Not checked in the free run.
- cites [20] Y Barak-Corren, 2017, Progressive prediction of hospitalisation in the emergency department: uncovering hidden patterns to improve patient flow, Emerg Med J source
  Not checked in the free run.
- cites [21] Y Barak-Corren, 2017, Early Prediction Model of Patient Hospitalization From the Pediatric Emergency Department, Pediatrics source
  Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductcitation
This study does not address the implementation and efficacy barriers present in clinical practice.
“Lastly, this study does not address the implementation and efficacy barriers present in clinical practice [53].”
Quote in the paperNot a substantial claim0 of 1 sources checked
- cites [53] SM Schenkel, 2018, Triage, Machine Learning, Algorithms, and Becoming the Borg, Ann Emerg Med
  Not checked in the free run.

13 of 13

The paper relies on this without support.
Gradient boosting and deep neural networks have not been applied to the task of predicting admission at ED triage to date.
“While gradient boosting and deep neural networks have been shown to be powerful tools for predictive modeling, neither has been applied to the task of predicting admission at ED triage to date.”
The paper relies on this without support.
The three EDs studied are representative of EDs generally, so the models' performance generalizes to other settings.
“Machine learning can robustly predict hospital admission using triage information and patient history.”
The paper relies on this without support.
The historical information available in the EHR at the time of triage is sufficiently complete and accurate to predict admission.
“For each patient visit, we collected a total of 972 variables, divided into major categories shown in Table 1.”
The paper relies on this without support.
The ED provider's prior decision is a valid ground truth for hospital admission.
“Throughout this study, we predict patient disposition by using the ED provider's prior decision as our true label.”
The paper relies on this without support.
The variables used in the models are available at the time of emergency department triage.
“Our study shows that machine learning can robustly predict hospital admission at emergency department (ED) triage”

5 of 5

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

The model's performance (AUROC 0.856) is state-of-the-art and comparable with recent admission models (AUROC 0.825-0.877).
“This represents state-of-the-art performance comparable with recent admission models (e.g. AUROC 0.825–0.87722–25).”
From Using explainable machine learning to characterise data drift and detect emergent health risks for emergency department admissions during COVID-19 (2021) · cites this paper as [24]
Hospital services can be managed more efficiently if accurate short-term forecasts for emergency demand are available.
“Hospital services can be managed more efficiently if accurate short-term forecasts for emergency demand are available8,9.”
From Machine learning for real-time aggregated prediction of hospital admission for emergency patients (2022) · cites this paper as [9]
Hong et al. showed that ML algorithms like gradient-boosted trees and deep neural networks, applied to a large EHR dataset of 972 variables, improved predictive performance.
“Hong et al.9 showed that ML algorithms like gradient-boosted trees and deep neural networks, applied to a large EHR dataset of 972 variables, improved predictive performance.”
From Machine learning for real-time aggregated prediction of hospital admission for emergency patients (2022) · cites this paper as [9]
Using XGBoost, Barak-Corren et al. achieved AUROC of 0.87-0.93 with a 60 minute model, compared with 0.89 here, and Hong et al. achieved 0.87 with triage data.
“Using XGBoost, Barak-Corren et al.31 achieved AUROC of 0.87–0.93 with a 60 minute model, compared with 0.89 here, and Hong et al9 achieved 0.87 with triage data.”
From Machine learning for real-time aggregated prediction of hospital admission for emergency patients (2022) · cites this paper as [9]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

Conflicting descriptions of how model cutoffs were chosen
Minor
The methods say Youden's index determined the cutoff for each model. Table 3 instead says every model's cutoff was set to a fixed specificity of 0.85, chosen using Youden's index only on the full XGBoost model.

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Self-citation
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Untitled work
(1950) · doi:10.1002/1097-0142(1950)3:1

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Authors: Hong, W. S., Haimovich, A. D., & Taylor, R. A.
Affiliations: Yale University (US)
Corresponding author: Taylor, R. Andrew
ORCID: Hong, Woo Suk
Haimovich, Adrian Daniel
Taylor, R. Andrew
Venue: PLOS ONE
Publisher: Public Library of Science (PLoS)
Published: 2018-07-20
Volume: 13
Issue: 7
Pages: e0201016
ISSN: 1932-6203 (ISSN-L)
DOI: 10.1371/journal.pone.0201016
PMC: PMC6054406
OpenAlex: W2884597820
Semantic Scholar: d012519a924e41aa7ff49d9b6be58033bd60fd9c
Cited by: 326
References: 58
Citations by year: 2026: 51 · 2025: 55 · 2024: 47 · 2023: 39 · 2022: 46 · 2021: 39 · 2020: 30 · 2019: 18 · 2018: 1
FWCI: 12.22
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: PLoS ONE (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
RePEc: Research Papers in Economics (submittedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Figshare (submittedVersion, cc-by)
Funders: National Institutes of Health (1F30CA196191, T32GM007205)
Yale School of Medicine
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Health Sciences › Medicine › Emergency Medicine › Emergency and Acute Care Studies
Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords: emergency department triage, hospital admission prediction, medical history, Emergency Severity Index, hospital utilization, XGBoost, deep neural network, logistic regression, feature importance, information gain, low-dimensional models, training data size, model generalization, clinical decision support
MeSH terms: Machine Learning, Adult, Emergency Service, Hospital, Female, Humans, Male, Middle Aged, Patient Admission, Patient Discharge, Triage, Models, Statistical

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Predicting hospital admission at emergency department triage using machine learning

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