Predicting hospital admission at the emergency department triage: A novel prediction model

Parker, Clare Allison; Liu, Nan; Wu, Stella Xinzi; Shen, Yuzeng; Lam, Sean Shao Wei; Ong, Marcus Eng Hock

doi:10.1016/j.ajem.2018.10.060

Cited by 100Emergency and Acute Care StudiesHealthcare Operations and Scheduling OptimizationMachine Learning in Healthcare

Predicting hospital admission at the emergency department triage: A novel prediction model

Parker, Clare Allison, Liu, Nan, Wu, Stella Xinzi, Shen, Yuzeng, Lam, Sean Shao Wei, Ong, Marcus Eng Hock·2019·The American Journal of Emergency Medicine

DOI 10.1016/j.ajem.2018.10.060

Open this paper in CoChat — free account

Abstract

Background Emergency department (ED) overcrowding is a growing international patient safety issue. A major contributor to overcrowding is long wait times for inpatient hospital admission. The objective of this study is to create a model that can predict a patient's need for hospital admission at the time of triage.Methods Retrospective observational study of electronic clinical records of all ED visits over ten years to a large urban hospital in Singapore. The data was randomly divided into a derivation set and a validation set. We used the derivation set to develop a logistic regression model that predicts probability of hospital admission for patients presenting to the ED. We tested the model on the validation set and evaluated the performance with receiver operating characteristic (ROC) curve analysis.Results A total of 1,232,016 visits were included for final analysis, of which 38.7% were admitted. Eight variables were included in the final model: age group, race, postal code, day of week, time of day, triage category, mode of arrival, and fever status. The model performed well on the validation set with an area under the curve of 0.825 (95% CI 0.824-0.827). Increasing age, increasing triage acuity, and mode of arrival via private patient transport were most predictive of the need for admission.Conclusions We developed a model that accurately predicts admission for patients presenting to the ED using demographic, administrative, and clinical data routinely collected at triage. Implementation of the model into the electronic health record could help reduce the burden of overcrowding.

Integrity signals

Registry

Crossref · 10.1016/j.ajem.2018.10.060

Cites

23 of 23 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

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 [25]

The Effect of Emergency Department Crowding on Clinically Oriented Outcomes
Bernstein, S. L., Aronsky, D., Duseja, R., Epstein, S., Handel, D., Hwang, U., McCarthy, M., John McConnell, K., Pines, J. M., Rathlev, N., et al. (2009) · Academic Emergency Medicine · doi:10.1111/j.1553-2712.2008.00295.x · Cited by 1,147 · see citing works
Overcrowding in the nation’s emergency departments: Complex causes and disturbing effects
Derlet, R. W., & Richards, J. R. (2000) · Annals of Emergency Medicine · doi:10.1016/s0196-0644(00)70105-3 · Cited by 1,052 · see citing works
Effect of Emergency Department Crowding on Outcomes of Admitted Patients
Sun, B., Hsia, R. Y.-J., Weiss, R. E., Zingmond, D. S., Liang, L.-J., Han, W., McCreath, H. E., & Asch, S. M. (2012) · Annals of Emergency Medicine · doi:10.1016/j.annemergmed.2012.10.026 · Cited by 716 · see citing works
Determinants of Patient Satisfaction and Willingness to Return With Emergency Care
SUN, B., Adams, J. G., Orav, E. J., Rucker, D. W., Brennan, T. J., & Burstin, H. R. (2000) · Annals of Emergency Medicine · doi:10.1016/s0196-0644(00)70003-5 · Cited by 352 · see citing works
Access block causes emergency department overcrowding and ambulance diversion in Perth, Western Australia
Fatovich, D. M. (2005) · Emergency Medicine Journal · doi:10.1136/emj.2004.018002 · Cited by 276 · see citing works
Systematic review of trends in emergency department attendances: an Australian perspective
Lowthian, J. A., Curtis, A. J., Cameron, P. A., Stoelwinder, J. U., Cooke, M. W., & McNeil, J. J. (2010) · Emergency Medicine Journal · doi:10.1136/emj.2010.099226 · Cited by 233 · see citing works
Emergency department contributors to ambulance diversion: A quantitative analysis
Schull, M. J., Lazier, K., Vermeulen, M. J., Mawhinney, S., & Morrison, L. J. (2003) · Annals of Emergency Medicine · doi:10.1067/mem.2003.23 · Cited by 176 · see citing works
Comparison of an Informally Structured Triage System, the Emergency Severity Index, and the Manchester Triage System to Distinguish Patient Priority in the Emergency Department
Storm-Versloot, M. N., Ubbink, D. T., Kappelhof, J., & Luitse, J. S. K. (2011) · Academic Emergency Medicine · doi:10.1111/j.1553-2712.2011.01122.x · Cited by 158 · see citing works
Predicting Hospital Admissions at Emergency Department Triage Using Routine Administrative Data: PREDICTING HOSPITAL ADMISSION AT ED TRIAGE
Sun, Y., Heng, B. H., Tay, S. Y., & Seow, E. (2011) · Academic Emergency Medicine · doi:10.1111/j.1553-2712.2011.01125.x · Cited by 154 · see citing works
Text mining approach to predict hospital admissions using early medical records from the emergency department
Lucini, F. R., Fogliatto, F. S., da Silveira, G. J. C., Neyeloff, J. L., Anzanello, M. J., Kuchenbecker, R. S., & Schaan, B. D. (2017) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2017.01.001 · Cited by 139 · see citing works
Predicting emergency department inpatient admissions to improve same-day patient flow
Peck, J. S., Benneyan, J. C., Nightingale, D. J., & Gaehde, S. A. (2012) · Academic Emergency Medicine · doi:10.1111/j.1553-2712.2012.01435.x · Cited by 138 · see citing works
Triage of geriatric patients in the emergency department: validity and survival with the Emergency Severity Index
Baumann, M. R., & Strout, T. D. (2007) · Annals of Emergency Medicine · doi:10.1016/j.annemergmed.2006.04.011 · Cited by 121 · see citing works
Predicting Hospital Admission and Returns to the Emergency Department for Elderly Patients
LaMantia, M. A., Platts‐Mills, T. F., Biese, K., Khandelwal, C., Forbach, C. R., Cairns, C. B., Busby‐Whitehead, J., & Kizer, J. S. (2010) · Academic Emergency Medicine · doi:10.1111/j.1553-2712.2009.00675.x · Cited by 119 · see citing works
A simple tool to predict admission at the time of triage
Cameron, A., Rodgers, K., Ireland, A., Jamdar, R., & McKay, G. A. (2015) · Emergency Medicine Journal · doi:10.1136/emermed-2013-203200 · Cited by 99 · see citing works
Emergency medicine
Fatovich, D. M. (2002) · BMJ · doi:10.1136/bmj.324.7343.958 · Cited by 79 · see citing works
Generalizability of a Simple Approach for Predicting Hospital Admission From an Emergency Department
Peck, J. S., Gaehde, S. A., Nightingale, D. J., Gelman, D. Y., Huckins, D. S., Lemons, M. F., Dickson, E. W., & Benneyan, J. C. (2013) · Academic Emergency Medicine · doi:10.1111/acem.12244 · Cited by 63 · see citing works
Predicting admission of patients by their presentation to the emergency department
Kim, S. W., Li, J. Y. Z., Hakendorf, P. H., Teubner, D. J., Ben-Tovim, D. I., & Thompson, C. H. (2014) · Emergency Medicine Australasia · doi:10.1111/1742-6723.12252 · Cited by 49 · see citing works
Building a Decision Support System for Inpatient Admission Prediction With the Manchester Triage System and Administrative Check-in Variables
Zlotnik, A., Alfaro, M. C., Pérez, M. C. P., Gallardo-Antolín, A., & Martínez, J. M. M. (2016) · CIN: Computers, Informatics, Nursing · doi:10.1097/cin.0000000000000230 · Cited by 45 · see citing works
A cost sensitive inpatient bed reservation approach to reduce emergency department boarding times
Qiu, S., Chinnam, R. B., Murat, A., Batarse, B., Neemuchwala, H., & Jordan, W. (2014) · Health Care Management Science · doi:10.1007/s10729-014-9283-1 · Cited by 34 · see citing works
Predicting hospital admission in a pediatric Emergency Department using an Artificial Neural Network.
Leegon, J., Jones, I. R., Lanaghan, K., & Aronsky, D. (2006) · Europe PMC (PubMed Central) · Cited by 33 · see citing works
Predicting admission at triage: are nurses better than a simple objective score?
Cameron, A., Ireland, A., McKay, G. A., Stark, A., & Lowe, D. J. (2016) · Emergency Medicine Journal · doi:10.1136/emermed-2014-204455 · Cited by 31 · see citing works
International Journal of Statistics in Medical Research
(2023) · International Journal of Statistics in Medical Research · doi:10.6000/1929-6029 · Cited by 18 · see citing works
Comparison of Glasgow Admission Prediction Score and Amb Score in predicting need for inpatient care
Cameron, A., Jones, D., Logan, E., O'Keeffe, C. A., Mason, S. M., & Lowe, D. J. (2018) · Emergency Medicine Journal · doi:10.1136/emermed-2017-207246 · Cited by 16 · see citing works

Flagged first, then by citations

Sorted by citations

Clinical Decision Support Systems for Triage in the Emergency Department using Intelligent Systems: a Review
Fernandes, M. P., Vieira, S. M., Leite, F., Palos, C., Finkelstein, S. N., & Sousa, J. M. C. (2019) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2019.101762 · Cited by 314 · see citing works
Using explainable machine learning to characterise data drift and detect emergent health risks for emergency department admissions during COVID-19
Duckworth, C., Chmiel, F. P., Burns, D. K., Zlatev, Z. D., White, N. M., Daniels, T. W. V., Kiuber, M., & Boniface, M. J. (2021) · Scientific Reports · doi:10.1038/s41598-021-02481-y · Cited by 109 · see citing works
Predicting Intensive Care Unit admission among patients presenting to the emergency department using machine learning and natural language processing
Fernandes, M. P., Mendes, R., Vieira, S. M., Leite, F., Palos, C., Johnson, A. E. W., Finkelstein, S. N., Horng, S., & Celi, L. A. (2020) · PLoS ONE · doi:10.1371/journal.pone.0229331 · Cited by 84 · see citing works
Benchmarking emergency department prediction models with machine learning and public electronic health records
Xie, F., Zhou, J. R., Lee, J. W., Tan, M., Li, S., Rajnthern, L. S. O., Chee, M. L., Chakraborty, B., Wong, A.-K. I., Dagan, A. S., et al. (2022) · Scientific Data · doi:10.1038/s41597-022-01782-9 · Cited by 84 · see citing works
Self-citation
Development and Assessment of an Interpretable Machine Learning Triage Tool for Estimating Mortality After Emergency Admissions
Xie, F., Ong, M. E. H., Liew, J. N. M. H., Tan, K. B. K., Ho, A. F. W., Nadarajan, G. D., Low, L. L., Kwan, Y. H., Goldstein, B. A., Matchar, D. B., et al. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2021.18467 · Cited by 68 · see citing works
Self-citation
Prediction of admission in pediatric emergency department with deep neural networks and triage textual data
Roquette, B. P., Nagano, H., Marujo, E. C., & Maiorano, A. C. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.03.012 · Cited by 70 · see citing works
Machine learning for developing a prediction model of hospital admission of emergency department patients: Hype or hope?
De Hond, A., Raven, W., Schinkelshoek, L., Gaakeer, M., Ter Avest, E., Sir, O., Lameijer, H., Hessels, R. A., Reijnen, R., De Jonge, E., et al. (2021) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2021.104496 · Cited by 66 · see citing works
Prediction of hospitalization using artificial intelligence for urgent patients in the emergency department
Lee, J.-T., Hsieh, C., Lin, C., Lin, Y.-J., & Kao, C. (2021) · Scientific Reports · doi:10.1038/s41598-021-98961-2 · Cited by 58 · see citing works
A comparative study of supervised machine learning approaches to predict patient triage outcomes in hospital emergency departments
Elhaj, H., Achour, N., Tania, M. H., & Açıksarı, K. (2023) · Array · doi:10.1016/j.array.2023.100281 · Cited by 48 · see citing works
Deep Learning to Predict Hospitalization at Triage: Integration of Structured Data and Unstructured Text
Arnaud, É., Elbattah, M., Gignon, M., & Dequen, G. (2020) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata50022.2020.9378073 · Cited by 46 · see citing works
Risk of mortality and cardiopulmonary arrest in critical patients presenting to the emergency department using machine learning and natural language processing
Fernandes, M. P., Mendes, R., Vieira, S. M., Leite, F., Palos, C., Johnson, A. E. W., Finkelstein, S. N., Horng, S., & Celi, L. A. (2020) · PLoS ONE · doi:10.1371/journal.pone.0230876 · Cited by 45 · see citing works
Development and Validation of Machine Learning Models to Predict Admission From Emergency Department to Inpatient and Intensive Care Units
Fenn, A., Davis, C., Buckland, D. M., Kapadia, N. N., Nichols, M., Gao, M. C., Knechtle, W. S., Balu, S., Sendak, M., & Theiling, J. (2021) · Annals of Emergency Medicine · doi:10.1016/j.annemergmed.2021.02.029 · Cited by 45 · see citing works
Emergency Department Capacity Planning: A Recurrent Neural Network and Simulation Approach
Nas, S., & Koyuncu, M. (2019) · Computational and Mathematical Methods in Medicine · doi:10.1155/2019/4359719 · Cited by 44 · see citing works
Predicting hospital admission for older emergency department patients: Insights from machine learning
Mowbray, F., Zargoush, M., Jones, A., de Wit, K., & Costa, A. P. (2020) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2020.104163 · Cited by 43 · see citing works
Implementation of Prediction Models in the Emergency Department from an Implementation Science Perspective—Determinants, Outcomes, and Real-World Impact: A Scoping Review
Chan, S. L., Lee, J. W., Ong, M. E. H., Siddiqui, F. J., Graves, N., Ho, A. F. W., & Liu, N. (2023) · Annals of Emergency Medicine · doi:10.1016/j.annemergmed.2023.02.001 · Cited by 42 · see citing works
Self-citation
Leveraging Large-Scale Electronic Health Records and Interpretable Machine Learning for Clinical Decision Making at the Emergency Department: Protocol for System Development and Validation
Liu, N., Xie, F., Siddiqui, F. J., Ho, A. F. W., Chakraborty, B., Nadarajan, G. D., Tan, K. B. K., & Ong, M. E. H. (2021) · JMIR Research Protocols · doi:10.2196/34201 · Cited by 35 · see citing works
Self-citation
Application of Machine Learning Predictive Models for Early Detection of Glaucoma Using Real World Data
Raju, M., Shanmugam, K. P., & Shyu, C. (2023) · Applied Sciences · doi:10.3390/app13042445 · Cited by 31 · see citing works
An integrated optimization and machine learning approach to predict the admission status of emergency patients
Ahmed, A., Ashour, O. M., Ali, H., & Firouz, M. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117314 · Cited by 31 · see citing works
SIM-PFED: A Simulation-Based Decision Making Model of Patient Flow for Improving Patient Throughput Time in Emergency Department
Hamza, N., Majid, M. B. A., & Hujainah, F. (2021) · IEEE Access · doi:10.1109/access.2021.3098625 · Cited by 31 · see citing works
Developing machine learning models to personalize care levels among emergency room patients for hospital admission
Nguyen, M., Corbin, C. K., Eulalio, T. Y., Ostberg, N. P., Machiraju, G., Marafino, B. J., Baiocchi, M., Rose, C. C., & Chen, J. H. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab118 · Cited by 29 · see citing works
The mediating role of patient satisfaction and perceived quality of healthcare in the emergency department
Abidova, A., da Silva, P. D., & Moreira, S. (2021) · Medicine · doi:10.1097/md.0000000000025133 · Cited by 20 · see citing works
Machine Learning in Medical Triage: A Predictive Model for Emergency Department Disposition
Feretzakis, G., Sakagianni, A., Αναστασίου, Α., Kapogianni, I., Tsoni, R., Koufopoulou, C., Karapiperis, D., Kaldis, V., Kalles, D., & Verykios, V. S. (2024) · Applied Sciences · doi:10.3390/app14156623 · Cited by 23 · see citing works
Examining the association between paramedic transport to the emergency department and hospital admission: a population-based cohort study
Strum, R. P., Mowbray, F., Worster, A., Tavares, W., Leyenaar, M. S., Correia, R. H., & Costa, A. P. (2021) · BMC Emergency Medicine · doi:10.1186/s12873-021-00507-2 · Cited by 21 · see citing works
Criticality and clinical department prediction of ED patients using machine learning based on heterogeneous medical data
Xiao, Y., Zhang, J., Chi, C., Ma, Y., & Song, A. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107390 · Cited by 20 · see citing works
Models Predicting Hospital Admission of Adult Patients Utilizing Prehospital Data: Systematic Review Using PROBAST and CHARMS
Monahan, A. C., & Feldman, S. S. (2021) · JMIR Medical Informatics · doi:10.2196/30022 · Cited by 19 · see citing works
Novel model for predicting inpatient mortality after emergency admission to hospital in Singapore: retrospective observational study
Xie, F., Liu, N., Wu, S. X., Ang, Y., Low, L. L., Ho, A. F. W., Lam, S. S. W., Matchar, D. B., Ong, M. E. H., & Chakraborty, B. (2019) · BMJ Open · doi:10.1136/bmjopen-2019-031382 · Cited by 16 · see citing works
Self-citation
Assessing the Generalizability of a Clinical Machine Learning Model Across Multiple Emergency Departments
Ryu, A. J., Brufau, S. R., Qian, R., Heaton, H. A., Nestler, D. M., Ayanian, S., & Kingsley, T. C. (2022) · Mayo Clinic Proceedings Innovations Quality & Outcomes · doi:10.1016/j.mayocpiqo.2022.03.003 · Cited by 18 · see citing works
An Artificial Intelligence–Based Framework for Predicting Emergency Department Overcrowding: Development and Evaluation Study
Vural, O., Ozaydin, B., Aram, K. Y., Booth, J. S., Lindsey, B. F., & Ahmed, A. (2025) · JMIR Medical Informatics · doi:10.2196/73960 · Cited by 17 · see citing works
Prediction of hospitalization and waiting time within 24 hours of emergency department patients with unstructured text data
Seo, H., Ahn, I., Gwon, H., Kang, H. J., Kim, Y., Cho, H. N., Choi, H., Kim, M., Han, J., Kee, G., et al. (2023) · Health Care Management Science · doi:10.1007/s10729-023-09660-5 · Cited by 17 · see citing works
Machine Learning-Based Model for Emergency Department Disposition at a Public Hospital
Sezik, S., Cingiz, M. Ö., & İbiş, E. (2025) · Applied Sciences · doi:10.3390/app15031628 · Cited by 16 · see citing works
A deep learning architecture for forecasting daily emergency department visits with acuity levels
Zhao, X., Li, K., Ang, C. K. E., Ho, A. F. W., Liu, N., Ong, M. E. H., & Cheong, K. H. (2022) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2022.112777 · Cited by 16 · see citing works
Self-citation
Predicting Hospital Ward Admission from the Emergency Department: A Systematic Review
Larburu, N., Azkue, L., & Kerexeta, J. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13050849 · Cited by 15 · see citing works
Triage Data-Driven Prediction Models for Hospital Admission of Emergency Department Patients: A Systematic Review
Shin, H. A., Kang, H., & Choi, M. (2025) · Healthcare Informatics Research · doi:10.4258/hir.2025.31.1.23 · Cited by 14 · see citing works
Institution-Specific Machine Learning Models for Prehospital Assessment to Predict Hospital Admission: Prediction Model Development Study
Shirakawa, T., Sonoo, T., Ogura, K., Fujimori, R., Hara, K., Goto, T., Hashimoto, H., Takahashi, Y., Naraba, H., & Nakamura, K. (2020) · JMIR Medical Informatics · doi:10.2196/20324 · Cited by 14 · see citing works
Excess Patient Visits for Cough and Pulmonary Disease at a Large US Health System in the Months Prior to the COVID-19 Pandemic: Time-Series Analysis
Elmore, J. G., Wang, P., Kerr, K. F., Schriger, D. L., Morrison, D. E., Brookmeyer, R., Pfeffer, M. A., Payne, T. H., & Currier, J. S. (2020) · Journal of Medical Internet Research · doi:10.2196/21562 · Cited by 14 · see citing works
Leveraging a large language model (LLM) to predict hospital admissions of emergency department patients
Pasquadibisceglie, V., Appice, A., Malerba, D., & Fiameni, G. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128224 · Cited by 12 · see citing works
Development of a low‐dimensional model to predict admissions from triage at a pediatric emergency department
Leonard, F., Gilligan, J., & Barrett, M. J. (2022) · Journal of the American College of Emergency Physicians Open · doi:10.1002/emp2.12779 · Cited by 11 · see citing works
A novel triage framework for emergency department based on machine learning paradigm
Menshawi, A. M., & Hassan, M. M. (2024) · Expert Systems · doi:10.1111/exsy.13735 · Cited by 11 · see citing works
Predicting Emergency Severity Index (ESI) level, hospital admission, and admitting ward in an emergency department using data-driven machine learning
Agius, S., Cassar, V., Magri, C. J., Khan, W. M., Al‐Jumeily, D., Caruana, G., & Topham, L. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-02941-9 · Cited by 11 · see citing works
Deploying predictive analytics to enhance patient agility and patient value in hospitals: A position paper and research proposal
Broekharst, D. S. E., van de Wetering, R., Ooms, W., Helms, R. W., & Roijakkers, N. (2023) · Healthcare Analytics · doi:10.1016/j.health.2023.100141 · Cited by 11 · see citing works
Modern Paediatric Emergency Department: Potential Improvements in Light of New Evidence
Kula, R., Popela, S., Klučka, J., Charwátová, D., Djakow, J., & Štourač, P. (2023) · Children · doi:10.3390/children10040741 · Cited by 10 · see citing works
Prospective External Validation of a Commercial Model Predicting the Likelihood of Inpatient Admission From the Emergency Department
Dadabhoy, F. Z., Driver, L., McEvoy, D. S., Stevens, R., Rubins, D., & Dutta, S. (2023) · Annals of Emergency Medicine · doi:10.1016/j.annemergmed.2022.11.012 · Cited by 10 · see citing works
A Machine Learning Pipeline Using KNIME to Predict Hospital Admission in the MIMIC-IV Database
Tsoni, R., Kaldis, V., Kapogianni, I., Sakagianni, A., Feretzakis, G., & Verykios, V. S. (2023) · International Conference on Information, Intelligence, Systems & Applications (IISA) · doi:10.1109/iisa59645.2023.10345903 · Cited by 10 · see citing works
Drinking from the Holy Grail—Does a Perfect Triage System Exist? And Where to Look for It?
Ingielewicz, A., Rychlik, P., & Siemiński, M. (2024) · Journal of Personalized Medicine · doi:10.3390/jpm14060590 · Cited by 9 · see citing works
A bibliometric analysis of clinical studies on artificial intelligence in emergency medicine
Limon, Ö., Bayram, B., Çetin, M., Limon, G., & Dirican, N. (2025) · Medicine · doi:10.1097/md.0000000000043282 · Cited by 10 · see citing works
Modeling Helping Behavior in Emergency Evacuations Using Volunteer’s Dilemma Game
Kwak, J., Lees, M., Cai, W., & Ong, M. E. H. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-50371-0_38 · Cited by 9 · see citing works
Self-citation
Development of an emergency department triage tool to predict admission or discharge for older adults
Abugroun, A., Awadalla, S., Singh, S. K., & Fang, M. C. (2025) · International Journal of Emergency Medicine · doi:10.1186/s12245-025-00825-3 · Cited by 8 · see citing works
Impact of medication characteristics and adverse drug events on hospital admission after an emergency department visit: Prospective cohort study
Lohan, L., Marin, G., Faucanié, M., Laureau, M., Macioce, V., Périer, D., Pinzani, V., Giraud, I., Castet‐Nicolas, A., Jalabert, A., et al. (2021) · International Journal of Clinical Practice · doi:10.1111/ijcp.14224 · Cited by 7 · see citing works
Reducing Crowding in Emergency Departments With Early Prediction of Hospital Admission of Adult Patients Using Biomarkers Collected at Triage: Retrospective Cohort Study
Monahan, A. C., Feldman, S. S., & Fitzgerald, T. (2022) · JMIR Bioinformatics and Biotechnology · doi:10.2196/38845 · Cited by 7 · see citing works
Statistical learning methodologies and admission prediction in an emergency department
Ratnovsky, A., Rozenes, S., Bloch, E., & Halpern, P. (2021) · Australasian Emergency Care · doi:10.1016/j.auec.2020.11.004 · Cited by 7 · see citing works
Predicting hospital admission from emergency department triage data for patients presenting with fall-related fractures
Pai, D. R., Rajan, B., Jairath, P., & Rosito, S. R. (2022) · Internal and Emergency Medicine · doi:10.1007/s11739-022-03100-y · Cited by 7 · see citing works
A NICE combination for predicting hospitalisation at the Emergency Department: a European multicentre observational study of febrile children
Borensztajn, D., Hagedoorn, N. N., Carrol, E. D., von Both, U., Dewez, J. E., Emonts, M., van der Flier, M., de Groot, R., Herberg, J. A., Kohlmaier, B., et al. (2021) · The Lancet Regional Health - Europe · doi:10.1016/j.lanepe.2021.100173 · Cited by 7 · see citing works
From predictions to recommendations: Tackling bottlenecks and overstaying in the Emergency Room through a sequence of Random Forests
Verdaasdonk, M. J. A., & de Carvalho, R. M. (2022) · Healthcare Analytics · doi:10.1016/j.health.2022.100040 · Cited by 6 · see citing works
Clinical Severity Level Prediction Based Optimal Medical Resource Allocation at Mass Casualty Incident
Olivia, D., Amrutha, C., Nayak, A., Balachandra, M., & Saxena, A. (2022) · IEEE Access · doi:10.1109/access.2022.3200489 · Cited by 6 · see citing works
Early detection of risk for clinical deterioration in emergency department patients: validation of a version of the National Early Warning Score 2 for use in Spain
Arévalo-Buitrago, P., Morales‐Cané, I., Luque, E. O., Godino-Rubio, M., Rodríguez‐Borrego, M. A., & López‐Soto, P. J. (2024) · Emergencias · doi:10.55633/s3me/e09.2022 · Cited by 6 · see citing works
Machine learning-driven prediction of hospital admissions using gradient boosting and GPT-2
Zhang, X., Wang, H., Yu, G., & Zhang, W. (2025) · Digital Health · doi:10.1177/20552076251331319 · Cited by 5 · see citing works
Predicting Admissions From a Paediatric Emergency Department – Protocol for Developing and Validating a Low-Dimensional Machine Learning Prediction Model
Leonard, F., Gilligan, J., & Barrett, M. J. (2021) · Frontiers in Big Data · doi:10.3389/fdata.2021.643558 · Cited by 5 · see citing works
Criticality index conducted in pediatric emergency department triage
Heyming, T. W., Knudsen-Robbins, C., Feaster, W. W., & Ehwerhemuepha, L. (2021) · The American Journal of Emergency Medicine · doi:10.1016/j.ajem.2021.05.004 · Cited by 5 · see citing works
Model to predict risk for hospital admission and indicate the safety of reverse triage in a hospital emergency department: a prospective validation study
Leey, C., Riveiro, J. G. Z., Arnau, A., Fernàndez-Puigbó, M., Sala-Barcons, E., & Gené, E. (2024) · Emergencias · doi:10.55633/s3me/e064.2022 · Cited by 5 · see citing works
Seizure Prediction Model in Acute Tramadol Poisoning; a Derivation and Validation study.
Bazmi, E., Behnoush, B., Nazari, S. H., Khodakarim, S., Behnoush, A. H., & Soori, H. (2020) · Archives of Academic Emergency Medicine · doi:10.22037/aaem.v8i1.723 · Cited by 4 · see citing works
Disposition Decision Support by Laboratory Based Outcome Prediction
Mueller, O. S., Rentsch, K., Nickel, C. H., & Bingisser, R. M. (2021) · Journal of Clinical Medicine · doi:10.3390/jcm10050939 · Cited by 4 · see citing works
Medical Admission Prediction Score (MAPS); a simple tool to predict medical admissions in the emergency department
Zahid, M., Khan, A. A., Ata, F., Yousaf, Z., Naushad, V. A., Purayil, N. K., Chandra, P., Singh, R., Kartha, A. B., Elzouki, A.-N. Y., et al. (2023) · PLoS ONE · doi:10.1371/journal.pone.0293140 · Cited by 4 · see citing works
Predicting emergency department disposition using machine learning and large language models to support proactive capacity management: a multicenter retrospective study
Zeinali, F., Taaffe, K., Gaafary, C., Jackson, W., Ramsay, M. A., Hobbs, J., & Pirrallo, R. G. (2026) · BMC Emergency Medicine · doi:10.1186/s12873-026-01487-x · Cited by 3 · see citing works
Using teletriage to model the risk of hospital admission at the time of registration in an emergency department
Arora, S., & Taylor, J. W. (2025) · Omega · doi:10.1016/j.omega.2025.103381 · Cited by 4 · see citing works
Comparison of Time Indicators and Outcome of Pre-hospital Emergency Operations in Two Methods of Electronic Registration with Asayar Program and Paper Registration
Gaeeni, M., Vernosfadarani, M. R., Massoud, M. P., & Hemta, A. (2021) · Qom Univ Med Sci J · doi:10.52547/qums.14.12.22 · Cited by 3 · see citing works
Designing Health Care Provider–Centered Emergency Department Interventions: Participatory Design Study
Seo, W., Li, J., Zhang, Z., Zheng, C., Singh, H., Pasupathy, K. S., Mahajan, P., & Park, S. Y. (2025) · JMIR Formative Research · doi:10.2196/68891 · Cited by 2 · see citing works
Integrative plasma metabolomic profiling identifies distinct vascular and inflammatory signatures in hospital emergency department patients
Chang, J. K., Jee, Y., Khaing, N. W., Chua, M. T., Choi, H., Kuan, W. S., Torta, F. T., & Wenk, M. R. (2025) · Clinica Chimica Acta · doi:10.1016/j.cca.2025.120562 · Cited by 2 · see citing works
Emergency department crowding in the modern era: a systematic review (2018–2025)
Oskvarek, J. J., Leubitz, A., Rahman, N., Sure, B., & Pines, J. M. (2026) · Clinical and Experimental Emergency Medicine · doi:10.15441/ceem.25.172 · Cited by 2 · see citing works
Enhancing Decision Making with Deep Reinforcement Learning in a Context of Novel Coronavirus Outbreak
Jiang, H., Wang, W. Y. C., Goh, T., & Zhu, J. (2020) · International Conference on Medical and Health Informatics (ICMHI) · doi:10.1145/3418094.3418137 · Cited by 2 · see citing works
Analysis Of Factors That Affect the Implementation Of Triage On Satisfaction Of Patients Family
Ullah, T. (2022) · Journal of Applied Nursing and Health · doi:10.55018/janh.v4i1.66 · Cited by 2 · see citing works
Efetividade do sistema de triagem em serviço de emergência privado
Previdelli, M. M., Liberato, A. A. P., Taninaga, M. I., Huss, C. N., & Alves, R. L. O. (2021) · Medicina (Ribeirão Preto) · doi:10.11606/issn.2176-7262.rmrp.2021.173954 · Cited by 2 · see citing works
Progressive prediction of hospitalisation and patient disposition in the emergency department
Uhl, L., Augusto, V., Lemaire, V., Alexandre, Y., Jardinaud, F., Bercelli, P., & Aloui, S. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020777 · Cited by 2 · see citing works
A new machine learning workflow to create an optimal waiting list in hospitals
Mbaye, M. M., Salem, F. M. H. A., & Tahiri, N. (2023) · International Conference on Medical and Health Informatics (ICMHI) · doi:10.1145/3608298.3608328 · Cited by 2 · see citing works
FACTORS ASSOCIATED WITH PROLONGED HOSPITAL STAY OF PATIENTS TREATED IN THE EMERGENCY DEPARTMENT
AKRAM, M., MAZHAR, H., & KHALID, M. (2024) · Biological and Clinical Sciences Research Journal · doi:10.54112/bcsrj.v2024i1.1027 · Cited by 1 · see citing works
Using Traditional and Deep Machine Learning to Predict Emergency Room Triage Levels
Yıldırım, M., Sezik, S., & Başar, A. (2025) · Journal of Computational Biology · doi:10.1089/cmb.2024.0632 · Cited by 1 · see citing works
Severity classification and disposition prediction using ensemble learning for home-based patient management with adequate decision making
Khatib, A. E., Abdallah, C. B., & Moh, A. N. S. (2025) · Array · doi:10.1016/j.array.2025.100453 · Cited by 1 · see citing works
Development of a Triage-Level Predictive Model for Hospitalization in the Emergency Department
Trotzky, D., Preisler, Y., Amoyal, A., Pachys, G., Mosery, J., Cohen, A., Avisar, S., & Baran, T. Z. (2026) · Journal of Clinical Medicine · doi:10.3390/jcm15051901 · Cited by 1 · see citing works
A Global and Local SHAP-Driven Interpretable Framework for Early Hospital Admission Prediction With Machine Learning at Emergency Department Triage
Brown, A. E., Marostica, C. W., Hodgson, N. R., & Martini, W. A. (2026) · Cureus · doi:10.7759/cureus.111163 · Cited by 1 · see citing works
Association Between the Simplest Clinical Factors and Emergency Department Dispositions: A Retrospective Observational Study
Satoh, K., Okuyama, M., & Nakae, H. (2021) · Cureus · doi:10.7759/cureus.12844 · Cited by 1 · see citing works
Modeling acute care utilization: practical implications for insomnia patients
Chekani, F., Zhu, Z., Khandker, R. K., Ai, J., Meng, W., Holler, E., Dexter, P. R., Boustani, M. A., & Miled, Z. B. (2023) · Scientific Reports · doi:10.1038/s41598-023-29366-6 · Cited by 1 · see citing works
Forecasting Hospital Mental-Health Admissions with a Novel Hybrid Deep Learning Architecture
Mossop, B., Bismil, R., & Rahman, Q. A. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385786 · Cited by 1 · see citing works
Optimizing Patient Triage in Emergency Department Reception: A Hybrid Algorithm Approach
Khatib, A. E., & Moh, A. N. S. (2024) · International Conference on Control, Decision and Information Technologies (CoDIT) · doi:10.1109/codit62066.2024.10708589 · Cited by 1 · see citing works
Social workers amidst the crisis of medical neoliberalism and welfare state: Perspectives of healthcare workers in private hospitals regarding social work needs in emergency departments in Türkiye
Güre, M. D. P., Uğurlu, Z., Karataş, M., Başcıllar, M., & Karakaya, C. (2025) · Social Work in Health Care · doi:10.1080/00981389.2025.2468280 · Cited by 1 · see citing works
Predicting Average Wait-Time of COVID-19 Test Results and Efficacy Using Machine Learning Algorithms
Hijry, H., Olawoyin, R., Edwards, W., McDonald, G. C., Debnath, D., & Al-Hejri, Y. (2021) · International Journal of Industrial Engineering and Operations Management · doi:10.46254/j.ieom.20210202 · Cited by 1 · see citing works
Predicting Hospital Admission by Adding Chief Complaints Using Machine Learning Approach
Wu, I., Chen, C.-E., Lin, Z.-R., Chen, T., & Feng, Y.-Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-05544-7_18 · Cited by 1 · see citing works
The Devil Is in the Implementation: Beyond Statistical Validation of the Epic Integrated Admission Prediction Model
Gingold, D. B. (2023) · Annals of Emergency Medicine · doi:10.1016/j.annemergmed.2022.12.025 · Cited by 1 · see citing works
Daily output measures of Emergency Department in association with seasonality and day of the week: A retrospective cohort observational study
Gabbay, U., & Drescher, M. J. (2022) · Medicine · doi:10.1097/md.0000000000030555
Forecasting Patient Arrivals in Emergency Departments Using Time Series Prediction Models: A Research at a Tunisian Emergency Department
Jmal, Y., & Yaacoub, S. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-87719-3_10
Automated Severity Classification for Patient Triage Using a Multilayer Perceptron Model in Emergency Departments
Khatib, A. E., & Moh, A. N. S. (2024) · Artificial Intelligence Revolutions (AIR) · doi:10.1109/air63653.2024.00018
Factors associated with disposition of patients transferred to an emergency department in a district hospital in Botswana
Mamalelala, T. T., Holzemer, W. L., Maleho, M. E., & Mokone, D. J. (2025) · International Emergency Nursing · doi:10.1016/j.ienj.2025.101624
Early prediction of hospital admission in the emergency department by generating expanded chief complaints
Feng, Y.-Y., Wu, I., Chen, T., Lin, Z.-R., Chin, L.-H., & Chang, W. (2025) · Intelligent Data Analysis · doi:10.1177/1088467x251340169
Validating emergency department admission predictions based on local data through MIMIC-IV
Meimeti, F., Triantafyllopoulos, L., Sakagianni, A., Kaldis, V., Tzelves, L. I., Theodorakis, N., Paxinou, E., Feretzakis, G., Kalles, D., & Verykios, V. S. (2025) · Intelligent Decision Technologies · doi:10.1177/18724981251340355
Development of a nomogram for triage admission prediction: A pilot study in Hong Kong
Weng, J., Cheung, C. C. L., Leung, K. Y., & Law, K. L. (2025) · Hong Kong Journal of Emergency Medicine · doi:10.1002/hkj2.12061
Additional file 1 of Examining the association between paramedic transport to the emergency department and hospital admission: a population-based cohort study
Strum, R. P., Mowbray, F., Worster, A., Tavares, W., Leyenaar, M. S., Correia, R. H., & Costa, A. P. (2021) · Figshare · doi:10.6084/m9.figshare.16800287
Routine laboratory data for predicting 30-day emergency department revisits: the AXIS-2 risk score
Demirci, F., & Demirci, A. (2026) · BMC Emergency Medicine · doi:10.1186/s12873-026-01496-w
AI‐enhanced decision support for fall patients: streamlining inpatient flow in the emergency department
Pai, D. R., Ananthakumar, U., & Sureshan, S. (2026) · International Transactions in Operational Research · doi:10.1111/itor.70182
Does Provider Identity at Triage Improve Machine Learning Prediction of Hospital Admission? A Comparative Analysis of Ten Supervised Classifiers with SHAP Explainability
Brown, A. E., Marostica, C. W., & Martini, W. A. (2026) · Journal of Personalized Medicine · doi:10.3390/jpm16040204
Hospital Services Quality Prediction Using A New Deep Fuzzy Learning Model
Al-khafaji, M. K., & Al-Shamery, E. S. (2026) · Kufa Journal of Engineering · doi:10.30572/2018/kje/170319
Machine Learning Model for Predicting Delayed Patient Placement after Emergency Admission Using Bed Assignment Logs, Isolation Needs, Unit Census, Nurse Staffing Levels, and Specialty Service Availability
Herrera, L., Rojas, D., & Castro, A. (2024) · Journal of Health Informatics and Digital Systems · doi:10.68159/p757158570
Artificial Intelligence-Enabled Hospital Command Center for Predicting Patient Transfer Bottlenecks Using Admission Requests, Unit Occupancy, Bed Cleaning Duration, Isolation Requirements, and Staffing Constraints
Walker, J., & Harris, O. (2023) · Journal of Health Informatics and Digital Systems · doi:10.68159/h134661506

Development and Validation of Models to Predict Hospital Admission for Emergency Department Patients
Xie, B. (2013) · International Journal of Statistics in Medical Research · doi:10.6000/1929-6029.2013.02.01.07 · Cited by 8 · see citing works
Triage Data-Driven Prediction Models for Hospital Admission of Emergency Department Patients: A Systematic Review
Shin, H. A., Kang, H., & Choi, M. (2025) · Healthcare Informatics Research · doi:10.4258/hir.2025.31.1.23 · Cited by 14 · see citing works
Predicting Hospital Admissions at Emergency Department Triage Using Routine Administrative Data: PREDICTING HOSPITAL ADMISSION AT ED TRIAGE
Sun, Y., Heng, B. H., Tay, S. Y., & Seow, E. (2011) · Academic Emergency Medicine · doi:10.1111/j.1553-2712.2011.01125.x · Cited by 154 · see citing works
P112: Predicting patient admission from the emergency department using triage administrative data
Savage, D. W., Weaver, B., & Wood, D. (2017) · CJEM · doi:10.1017/cem.2017.314 · Cited by 2 · see citing works
Development of a nomogram for triage admission prediction: A pilot study in Hong Kong
Weng, J., Cheung, C. C. L., Leung, K. Y., & Law, K. L. (2025) · Hong Kong Journal of Emergency Medicine · doi:10.1002/hkj2.12061

Authors: Parker, C. A., Liu, N., Wu, S. X., Shen, Y., Lam, S. S. W., & Ong, M. E. H.
Affiliations: Duke University (US)
SingHealth (SG)
National University of Singapore (SG)
Corresponding author: Liu, Nan
ORCID: Liu, Nan
Shen, Yuzeng
Lam, Sean Shao Wei
Ong, Marcus Eng Hock
Venue: The American Journal of Emergency Medicine
Publisher: Elsevier BV
Published: 2019-08
Volume: 37
Issue: 8
Pages: 1498-1504
ISSN: 0735-6757 (ISSN-L)
DOI: 10.1016/j.ajem.2018.10.060
OpenAlex: W2898892062
Cited by: 100
References: 23
Citations by year: 2026: 8 · 2025: 19 · 2024: 9 · 2023: 16 · 2022: 13 · 2021: 22 · 2020: 10 · 2019: 3
FWCI: 10.79
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
Locations: The American Journal of Emergency Medicine (publishedVersion)
PubMed (publishedVersion)
Topics: Health Sciences › Medicine › Emergency Medicine › Emergency and Acute Care Studies
Health Sciences › Health Professions › Emergency Medical Services › Healthcare Operations and Scheduling Optimization
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Keywords: hospital admission prediction, emergency department overcrowding, emergency department triage, logistic regression, triage acuity, mode of arrival, patient transport, fever, electronic health record implementation, patient safety
MeSH terms: Adult, Aged, Aged, 80 and over, Cross-Sectional Studies, Decision Support Techniques, Emergency Service, Hospital, Female, Hospitalization, Humans, Male, Middle Aged, Retrospective Studies, ROC Curve, Singapore, Triage, Logistic Models, Risk Assessment, Young Adult

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Predicting hospital admission at the emergency department triage: A novel prediction model

Abstract

Integrity signals

Claims the paper makes

Claims other papers make about this one

Cites

Cited by

Related

Record

About these checks