EN

Machine Learning for E-triage

Öz

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.

Anahtar Kelimeler

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Mühendislik

Bölüm

Araştırma Makalesi

Yayımlanma Tarihi

20 Temmuz 2022

Gönderilme Tarihi

23 Mayıs 2022

Kabul Tarihi

29 Haziran 2022

Yayımlandığı Sayı

Yıl 2022 Cilt: 6 Sayı: 1

Kaynak Göster

APA
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, 6(1), 86-90. https://izlik.org/JA37BZ43GA
AMA
1.Bora Ş, Kantarcı A, Erdoğan A, vd. Machine Learning for E-triage. IJMSIT. 2022;6(1):86-90. https://izlik.org/JA37BZ43GA
Chicago
Bora, Şebnem, Aylin Kantarcı, Arife Erdoğan, vd. 2022. “Machine Learning for E-triage”. International Journal of Multidisciplinary Studies and Innovative Technologies 6 (1): 86-90. https://izlik.org/JA37BZ43GA.
EndNote
Bora Ş, Kantarcı A, Erdoğan A, Beynek B, Kheibari B, Evren V, Erdoğan MA, Kavak F, Afyoncu F, Eryaz C, Gönüllü H (01 Temmuz 2022) Machine Learning for E-triage. International Journal of Multidisciplinary Studies and Innovative Technologies 6 1 86–90.
IEEE
[1]Ş. Bora vd., “Machine Learning for E-triage”, IJMSIT, c. 6, sy 1, ss. 86–90, Tem. 2022, [çevrimiçi]. Erişim adresi: https://izlik.org/JA37BZ43GA
ISNAD
Bora, Şebnem - Kantarcı, Aylin - Erdoğan, Arife - Beynek, Burak - Kheibari, Bita - Evren, Vedat - Erdoğan, Mümin Alper v.dğr. “Machine Learning for E-triage”. International Journal of Multidisciplinary Studies and Innovative Technologies 6/1 (01 Temmuz 2022): 86-90. https://izlik.org/JA37BZ43GA.
JAMA
1.Bora Ş, Kantarcı A, Erdoğan A, Beynek B, Kheibari B, Evren V, Erdoğan MA, Kavak F, Afyoncu F, Eryaz C, Gönüllü H. Machine Learning for E-triage. IJMSIT. 2022;6:86–90.
MLA
Bora, Şebnem, vd. “Machine Learning for E-triage”. International Journal of Multidisciplinary Studies and Innovative Technologies, c. 6, sy 1, Temmuz 2022, ss. 86-90, https://izlik.org/JA37BZ43GA.
Vancouver
1.Şebnem Bora, Aylin Kantarcı, Arife Erdoğan, Burak Beynek, Bita Kheibari, Vedat Evren, Mümin Alper Erdoğan, Fulya Kavak, Fatmanur Afyoncu, Cansu Eryaz, Hayriye Gönüllü. Machine Learning for E-triage. IJMSIT [Internet]. 01 Temmuz 2022;6(1):86-90. Erişim adresi: https://izlik.org/JA37BZ43GA