Estimation of the future fracture epidemiology in the patients applying to the emergency department with long short time memory method

Ozhan Pazarcı; Yunus Torun; Serkan Akkoyun

doi:10.17776/csj.730441

EN

Estimation of the future fracture epidemiology in the patients applying to the emergency department with long short time memory method

Abstract

Operation rooms, human resources and equipment planning are essential for increasing the effectiveness of diagnostic and treatment methods in line with the needs of emergency cases. In this study, 151822 patients admitted to the emergency department (ED) within 3 years were examined in three categories including gender, fracture sites and causes of fracture. However, fracture cases were treated as time series and Long Short Time Memory (LSTM) method was used to estimate the number of future fracture cases. In the learning phase, the number of monthly cases in the next 6 months was estimated using 30-month case numbers. The Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean relative Error (MRE) values of the error rate between the estimated and actual number of cases were given.

Keywords

References

Kosuge D, Barry M. Changing trends in the management of; children’s fractures. Bone and Joint Journal, 97(4) (2015) 442-448.
Court-Brown CM, Caesar B. Epidemiology of adult fractures: A review. Injury, 37(8) (2006) 691-697.
Xia J, Pan S, Zhu M, Cai G, Yan M, Su Q, et al. A Long Short-Term Memory Ensemble Approach for Improving the Outcome Prediction in Intensive Care Unit. Computational and Mathematical Methods in Medicine, 2019 (2019) 1–10.
Pham T, Tran T, Phung D, Venkatesh S. Predicting healthcare trajectories from medical records: A deep learning approach. Journal of Biomedical Informatics, 69(2) (2017) 18–29.
Olczak J, Fahlberg N, Maki A, Razavian AS, Jilert A, Stark A, et al. Artificial intelligence for analyzing orthopedic trauma radiographs: Deep learning algorithms—are they on par with humans for diagnosing fractures? Acta Orthopaedica, 88 (2017) 581–586.
Lindsey R, Daluiski A, Chopra S, Lachapelle A, Mozer M, Sicular S, et al. Deep neural network improves fracture detection by clinicians. Proceedings of the National Academy of Sciences of the United States of America, 115(45) (2018) 11591-11596.
Cheng CT, Ho TY, Lee TY, Chang CC, Chou CC, Chen CC, et al. Application of a deep learning algorithm for detection and visualization of hip fractures on plain pelvic radiographs. European Radiology, 29 (2019) 5469-5477.
Choi E, Bahadori MT, Schuetz A, Stewart WF, Sun J. Doctor AI: Predicting Clinical Events via Recurrent Neural Networks. JMLR Workshop and Conference Proceedings 2015.

Details

Primary Language

English

Subjects

-

Journal Section

Research Article

Authors

Ozhan Pazarcı
0000-0002-2345-0827
Türkiye

Yunus Torun
0000-0002-6187-0451
Türkiye

Serkan Akkoyun ^*
0000-0002-8996-3385
Türkiye

Publication Date

September 30, 2020

Submission Date

May 1, 2020

Acceptance Date

September 10, 2020

Published in Issue

Year 2020 Volume: 41 Number: 3

DOI

https://doi.org/10.17776/csj.730441

IZ

https://izlik.org/JA82FU45GJ

Cite

RIS / Bibtex

APA

Pazarcı, O., Torun, Y., & Akkoyun, S. (2020). Estimation of the future fracture epidemiology in the patients applying to the emergency department with long short time memory method. Cumhuriyet Science Journal, 41(3), 741-746. https://doi.org/10.17776/csj.730441

AMA

1.Pazarcı O, Torun Y, Akkoyun S. Estimation of the future fracture epidemiology in the patients applying to the emergency department with long short time memory method. CSJ. 2020;41(3):741-746. doi:10.17776/csj.730441

Chicago

Pazarcı, Ozhan, Yunus Torun, and Serkan Akkoyun. 2020. “Estimation of the Future Fracture Epidemiology in the Patients Applying to the Emergency Department With Long Short Time Memory Method”. Cumhuriyet Science Journal 41 (3): 741-46. https://doi.org/10.17776/csj.730441.

EndNote

Pazarcı O, Torun Y, Akkoyun S (September 1, 2020) Estimation of the future fracture epidemiology in the patients applying to the emergency department with long short time memory method. Cumhuriyet Science Journal 41 3 741–746.

IEEE

[1]O. Pazarcı, Y. Torun, and S. Akkoyun, “Estimation of the future fracture epidemiology in the patients applying to the emergency department with long short time memory method”, CSJ, vol. 41, no. 3, pp. 741–746, Sept. 2020, doi: 10.17776/csj.730441.

ISNAD

Pazarcı, Ozhan - Torun, Yunus - Akkoyun, Serkan. “Estimation of the Future Fracture Epidemiology in the Patients Applying to the Emergency Department With Long Short Time Memory Method”. Cumhuriyet Science Journal 41/3 (September 1, 2020): 741-746. https://doi.org/10.17776/csj.730441.

JAMA

1.Pazarcı O, Torun Y, Akkoyun S. Estimation of the future fracture epidemiology in the patients applying to the emergency department with long short time memory method. CSJ. 2020;41:741–746.

MLA

Pazarcı, Ozhan, et al. “Estimation of the Future Fracture Epidemiology in the Patients Applying to the Emergency Department With Long Short Time Memory Method”. Cumhuriyet Science Journal, vol. 41, no. 3, Sept. 2020, pp. 741-6, doi:10.17776/csj.730441.

Vancouver

1.Ozhan Pazarcı, Yunus Torun, Serkan Akkoyun. Estimation of the future fracture epidemiology in the patients applying to the emergency department with long short time memory method. CSJ. 2020 Sep. 1;41(3):741-6. doi:10.17776/csj.730441