| Autor: |
Chu J; Department of Computer Science and Information Engineering, Fu Jen Catholic University, New Taipei City 242062, Taiwan., Hsieh CH; Department of General Surgery, Shin Kong Wu Ho Su Memorial Hospital, Taipei 111045, Taiwan., Shih YN; Department of Computer Science and Information Engineering, Fu Jen Catholic University, New Taipei City 242062, Taiwan., Wu CC; Graduate Institute of Applied Science and Engineering, Fu Jen Catholic University, New Taipei City 242062, Taiwan., Singaravelan A; Graduate Institute of Applied Science and Engineering, Fu Jen Catholic University, New Taipei City 242062, Taiwan., Hung LP; National Taipei University of Nursing and Health Sciences, Taipei City 112, Taiwan., Hsu JL; Department of Computer Science and Information Engineering, Fu Jen Catholic University, New Taipei City 242062, Taiwan. |
| Abstrakt: |
Effectively handling the limited number of surgery operating rooms equipped with expensive equipment is a challenging task for hospital management such as reducing the case-time duration and reducing idle time. Improving the efficiency of operating room usage via reducing the idle time with better scheduling would rely on accurate estimation of surgery duration. Our model can achieve a good prediction result on surgery duration with a dozen of features. We have found the result of our best performing department-specific XGBoost model with the values 31.6 min, 18.71 min, 0.71, 28% and 27% for the metrics of root-mean-square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), mean absolute percentage error (MAPE) and proportion of estimated result within 10% variation, respectively. We have presented each department-specific result with our estimated results between 5 and 10 min deviation would be more informative to the users in the real application. Our study shows comparable performance with previous studies, and the machine learning methods use fewer features that are better suited for universal usability. |
