Research Article
Year 2020,
, 741 - 746, 30.09.2020
Abstract
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.
- Maragatham G, Devi S. LSTM Model for Prediction of Heart Failure in Big Data. Journal of Medical Systems, 43 (2019) 111.
- Golas SB, Shibahara T, Agboola S, Otaki H, Sato J, Nakae T, et al. A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data. BMC Medical Informatics and Decision Making, 18 (2018) 44.
- James AP. Deep Learning Classifiers with Memristive Networks. vol. 14. Cham: Springer International Publishing; 2020.
- Hochreiter S, Schmidhuber J. Long Short-Term Memory. Neural Computation 1997.
- Gers FA, Schmidhuber J. Recurrent nets that time and count. Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, IEEE, 3 (2000) 189–94.
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.
- Maragatham G, Devi S. LSTM Model for Prediction of Heart Failure in Big Data. Journal of Medical Systems, 43 (2019) 111.
- Golas SB, Shibahara T, Agboola S, Otaki H, Sato J, Nakae T, et al. A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data. BMC Medical Informatics and Decision Making, 18 (2018) 44.
- James AP. Deep Learning Classifiers with Memristive Networks. vol. 14. Cham: Springer International Publishing; 2020.
- Hochreiter S, Schmidhuber J. Long Short-Term Memory. Neural Computation 1997.
- Gers FA, Schmidhuber J. Recurrent nets that time and count. Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, IEEE, 3 (2000) 189–94.
There are 13 citations in total.
Details
| Primary Language | English |
|---|---|
| Journal Section | Engineering Sciences |
| Authors | |
| Publication Date | September 30, 2020 |
| Submission Date | May 1, 2020 |
| Acceptance Date | September 10, 2020 |
| Published in Issue | Year 2020 |