SavedAdd your journal

Journals / International Journal of Multidisciplinary Studies and Innovative Technologies / 2026 / Cilt: 6 Sayı: 1

Machine Learning for E-triage

Şebnem Bora(EGE UNIVERSITY, FACULTY OF ENGINEERING, DEPARTMENT OF COMPUTER ENGINEERING)0000-0003-0111-4635
Aylin Kantarcı(EGE UNIVERSITY, FACULTY OF ENGINEERING, DEPARTMENT OF COMPUTER ENGINEERING)0000-0001-7019-1613
Arife Erdoğan(IZMIR BAKIRCAY UNIVERSITY, SCHOOL OF MEDICINE)0000-0003-2488-2012
Burak Beynek(KIRKLARELI UNIVERSITY)0000-0003-3395-0451
Bita Kheibari(EGE UNIVERSITY, INSTITUTE OF SCIENCE, COMPUTER ENGINEERING (DR))0000-0002-1930-2311
Vedat Evren(EGE UNIVERSITY, EGE FACULTY OF MEDICINE)0000-0003-0274-0427
Mümin Alper Erdoğan(IZMIR KATIP CELEBI UNIVERSITY, SCHOOL OF MEDICINE)0000-0003-0048-444X
Fulya Kavak(EGE UNIVERSITY, INSTITUTE OF SCIENCE, COMPUTER ENGINEERING (MASTER) (WITH THESIS))0000-0002-1938-9293
Fatmanur Afyoncu(EGE UNIVERSITY, INSTITUTE OF SCIENCE, COMPUTER ENGINEERING (MASTER) (WITH THESIS))0000-0002-6580-8735
Cansu Eryaz(IZMIR BAKIRCAY UNIVERSITY, SCHOOL OF MEDICINE)0000-0002-7246-8847
Hayriye Gönüllü(IZMIR BAKIRCAY UNIVERSITY, SCHOOL OF MEDICINE)0000-0002-9587-6261
Pages
86–90
Publication Date
2022-07-20
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Our experiments show that Random Forest approach outperforms the others in e-triage.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Due to the rising number of visits to emergency departments all around the world and the importance of emergency departments in hospitals, the accurate and timely evaluation of a patient in the emergency section is of great importance.
📊
Key Findings & Data:Correct and timely triage of patients is vital to effective performance in the emergency department, and if the inappropriate level of triage is chosen, errors in patients' triage will have serious consequences.
💡
Conclusion & Impact:Our experiments show that Random Forest approach outperforms the others in e-triage.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Triage · Machine Learning · Emergency Department

Abstract

Due to the rising number of visits to emergency departments all around the world and the importance of emergency departments in hospitals, the accurate and timely evaluation of a patient in the emergency section is of great importance. In this regard, the correct triage of the emergency department also requires a high level of priority and sensitivity. Correct and timely triage of patients is vital to effective performance in the emergency department, and if the inappropriate level of triage is chosen, errors in patients' triage will have serious consequences. It can be difficult for medical staff to assess patients' priorities at times, therefore offering an intelligent method will be pivotal for both increasing the accuracy of patients' priorities and decreasing the waiting time for emergency patients. In this study, we evaluate the machine learning algorithms in triage procedure. Our experiments show that Random Forest approach outperforms the others in e-triage.

🔖

Cite This Article

Bora, Ş., Kantarcı, A., Erdoğan, A., Beynek, B., Kheibari, B., Evren, V., Erdoğan, M. A., Kavak, F., Afyoncu, F., Eryaz, C., Gönüllü, H. (2022). Machine Learning for E-triage. International Journal of Multidisciplinary Studies and Innovative Technologies, Cilt: 6 Sayı: 1, 86–90. https://ajindex.com/articles/1496981

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (25)

  1. M. Oberlin, E. Andr`es, M. Behr, S Kepka, L. Borgne, P., Bilbault,, “Emergency overcrowding and hospital organization: Causes and solutions”, La Revue de Médecine Interne, vol. 41(10),pp. 693-699, 2020.
  2. Ministry of Health of the Republic of Turkey. “Yataklı Sağlık Tesislerinde Acil Servis Hizmetlerinin Uygulama Usul Ve Esasları Hakkında Tebliğ”. https://www.saglik.gov.tr/TR,11321/yatakli-saglik-tesislerinde-acil-servis-hizmetlerinin-uygulama-usul-ve-esaslari-hakkinda-teblig.html, Access date: 22 April 2022
  3. H. M. Buschorn, T. D. Strout, J. M. Sholl, M. R. Baumann and G. Junction, “Emergency Medical Services Triage Using the Emergency Severity Index: Is it Reliable and Valid?”, Journal of Emergency Nursing, vol. 39(5), pp. 55-63, 2013.
  4. M. Christ, F. Grossmann, D. Winter,, R. Bingisser and E. Platz “Modern triage in the emergency department”, Deutsches Ärzteblatt International, vol. 107(50), pp. 892, 2010.
  5. S. Levin, M. Toerper, E. Hamrock, J. S. Hinson, S. Barnes, A. Dugas, B. Linton, T. Kirscj and G. Kelen “Machine Learning based electronic triage more accurately differentiates patients with respect to clinicaloutcomes compared with the emergency severity index”, 71(5), Annals of Emergency Medicine, pp. 565-574, 2018.
  6. A. W. Choi, T. Ko K.J. Hong and K. H. Kim,.“Machine learning-based prediction of the Korean triage and acuity scale level in emergency department patients”, Healthcare Informatics Research, vol. 25(4), pp. 305-312, 2019.
  7. S. Bong, L. H. Kim, H. Kim, C. Kang, S.H. Lee, J. H. Jeong, S. C. Kim, Y. J Park. and D. Lim, “Emergency department triageearly warning score (TWERS) predicts in-hospital mortality in the emergency department”, The American Journal of Emergency Medicine, vol. 38(2), pp. 203-210,2020.
  8. J. M. Kwon, Y. Lee, S. Lee, H. Park, J. Park, “Validation of deep learning based triage and acquity score using a large national dataset”, PLoS One,vol. 13(10), 2018.
  9. M. K. Patil,, S. D. Sawarkar and, M. S. Narwane, “Designing a model to detect diabetes using machine learning”, Int. J. Eng. Res. Technol, 8(11), 333-340,2019.
  10. L. Cunhe and W. Chenggang,"A new semi-supervised support vector machine learning algorithm based on active learning", 2nd International Conference on Future Computer and Communication, pp. 638-641, 2010.
  11. V. Vapnik, The Nature of Statistical Learning Theory. New York:Springer-verlag, 2000.
  12. T. Cover and P. Hart, "Nearest neighbor pattern classification", IEEE transactions on information theory, vol. 13(1),pp. 21-27, 1967
  13. T. Hastie, R. Tibshirani and J. H. Friedman, The elements of statistical learning: data mining, inference, and prediction, New York Springer, 2009.
  14. Bounsaythip, C., Rinta-Runsala, E. 2001. “Overview of data mining for customer behavior modeling”, VTT Information Tech. Rep., vol 1, pp.1-53, 2001.
  15. G. Biau and E. Scornet, “A random forest guided tour”, Test, vol 25(2), pp. 197-227, 2016.
  16. S. Raschka, Python Machine Learning.UK: Pack Publishing, 2015.
  17. J. R. Quinlan, C4.5 Programs for Machine Learning, USA, Morgan Kauffman, 1993
  18. L. Breiman, “Random Forests”, Machine Learning, vol. 45, pp. 5-32,Springer,2001.
  19. J. Mingers, “An empirical comparison of pruning methods for decision tree induction”, Machine Learning, vol. 4, pp. 227-243, 1989.
  20. M. Pal, “Random forest classifier for remote sensing classification”, International journal of remote sensing, 26(1), 217-222, 2005.
  21. M. Oberlin, E. Andr`es, M. Behr, S Kepka, L. Borgne, P., Bilbault,, “Emergency overcrowding and hospital organization: Causes and solutions”, La Revue de Médecine Interne, vol. 41(10),pp. 693-699, 2020.
  22. S. Bong, L. H. Kim, H. Kim, C. Kang, S.H. Lee, J. H. Jeong, S. C. Kim, Y. J Park. and D. Lim, “Emergency department triageearly warning score (TWERS) predicts in-hospital mortality in the emergency department”, The American Journal of Emergency Medicine, vol. 38(2), pp. 203-210,2020.
  23. M. K. Patil,, S. D. Sawarkar and, M. S. Narwane, “Designing a model to detect diabetes using machine learning”, Int. J. Eng. Res. Technol, 8(11), 333-340,2019.
  24. G. Biau and E. Scornet, “A random forest guided tour”, Test, vol 25(2), pp. 197-227, 2016.
  25. J. R. Quinlan, C4.5 Programs for Machine Learning, USA, Morgan Kauffman, 1993