Supplemental Material - Endoscopic Lumbar Interbody Fusion, Minimally Invasive Transforaminal Lumbar Interbody Fusion, and Open Transforaminal Lumbar Interbody Fusion for the Treatment of Lumbar Degenerative Diseases: A Systematic Review and Network Meta-Analysis

Supplemental Material for Endoscopic Lumbar Interbody Fusion, Minimally Invasive Transforaminal Lumbar Interbody Fusion, and Open Transforaminal Lumbar Interbody Fusion for the Treatment of Lumbar Degenerative Diseases: A Systematic Review and Network Meta-Analysis by Xijian Hu, Lei Yan, Xinjie Jin, Haifeng Liu, Jing Chai, and Bin Zhao in Global Spine Journal</p

The structure of GRU cell.

BackgroundReasonable and accurate forecasting of outpatient visits helps hospital managers optimize the allocation of medical resources, facilitates fine hospital management, and is of great significance in improving hospital efficiency and treatment capacity.MethodsBased on conjunctivitis outpatient data from the First Affiliated Hospital of Xinjiang Medical University Ophthalmology from 2017/1/1 to 2019/12/31, this paper built and evaluated Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models for outpatient visits prediction.ResultsIn predicting the number of conjunctivitis visits over the next 31 days, the LSTM model had a root mean square error (RMSE) of 2.86 and a mean absolute error (MAE) of 2.39, the GRU model has an RMSE of 2.60 and an MAE of 1.99.ConclusionsThe GRU method can better predict trends in hospital outpatient flow over time, thus providing decision support for medical staff and outpatient management.</div

The forecasting performance of the two models.

BackgroundReasonable and accurate forecasting of outpatient visits helps hospital managers optimize the allocation of medical resources, facilitates fine hospital management, and is of great significance in improving hospital efficiency and treatment capacity.MethodsBased on conjunctivitis outpatient data from the First Affiliated Hospital of Xinjiang Medical University Ophthalmology from 2017/1/1 to 2019/12/31, this paper built and evaluated Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models for outpatient visits prediction.ResultsIn predicting the number of conjunctivitis visits over the next 31 days, the LSTM model had a root mean square error (RMSE) of 2.86 and a mean absolute error (MAE) of 2.39, the GRU model has an RMSE of 2.60 and an MAE of 1.99.ConclusionsThe GRU method can better predict trends in hospital outpatient flow over time, thus providing decision support for medical staff and outpatient management.</div

S1 Data -

BackgroundReasonable and accurate forecasting of outpatient visits helps hospital managers optimize the allocation of medical resources, facilitates fine hospital management, and is of great significance in improving hospital efficiency and treatment capacity.MethodsBased on conjunctivitis outpatient data from the First Affiliated Hospital of Xinjiang Medical University Ophthalmology from 2017/1/1 to 2019/12/31, this paper built and evaluated Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models for outpatient visits prediction.ResultsIn predicting the number of conjunctivitis visits over the next 31 days, the LSTM model had a root mean square error (RMSE) of 2.86 and a mean absolute error (MAE) of 2.39, the GRU model has an RMSE of 2.60 and an MAE of 1.99.ConclusionsThe GRU method can better predict trends in hospital outpatient flow over time, thus providing decision support for medical staff and outpatient management.</div

Comparison of the LSTM and GRU models in the validation set.

Comparison of the LSTM and GRU models in the validation set.</p

Comparison of the LSTM and GRU models in the training set.

Comparison of the LSTM and GRU models in the training set.</p

Volume of each day’s conjunctivitis clinic from January 1, 2017 to December 31, 2019.

Volume of each day’s conjunctivitis clinic from January 1, 2017 to December 31, 2019.</p

The actual daily incidence of conjunctivitis and values predicted by the two models in December 2019.

The actual daily incidence of conjunctivitis and values predicted by the two models in December 2019.</p

The structure of LSTM cell.

BackgroundReasonable and accurate forecasting of outpatient visits helps hospital managers optimize the allocation of medical resources, facilitates fine hospital management, and is of great significance in improving hospital efficiency and treatment capacity.MethodsBased on conjunctivitis outpatient data from the First Affiliated Hospital of Xinjiang Medical University Ophthalmology from 2017/1/1 to 2019/12/31, this paper built and evaluated Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models for outpatient visits prediction.ResultsIn predicting the number of conjunctivitis visits over the next 31 days, the LSTM model had a root mean square error (RMSE) of 2.86 and a mean absolute error (MAE) of 2.39, the GRU model has an RMSE of 2.60 and an MAE of 1.99.ConclusionsThe GRU method can better predict trends in hospital outpatient flow over time, thus providing decision support for medical staff and outpatient management.</div