Forecasting of Daily Outpatient Visits Based on Genetic Programming

• الحاوية / القاعدة: Iranian Journal of Public Health
• المؤلفون الرئيسيون: Xiaobing Liu, Fulai Gu, Zhaoyang Bai, Qiyang Huang, Ge Ma
• التنسيق: مقال
• اللغة: الإنجليزية
• منشور في: Tehran University of Medical Sciences 2022-06-01
• الموضوعات: Daily outpatient visits; Forecasting; Time series data; Genetic programming; Outlier analysis
• الوصول للمادة أونلاين: https://ijph.tums.ac.ir/index.php/ijph/article/view/21805

Background: The forecasting of daily outpatient visits has significant practical implications in outpatient clinic operation management, not only contributing to guiding long-term resource planning and scheduling but also making tactical resolutions for short-term adjustments on special days such as holidays. We here in propose an effective genetic programming (GP)-based forecasting model to predict daily outpatient visits (OV) in a primary hospital. Methods: In the GP-based model, the holiday-based distance outlier mining algorithm was used to determine the holiday effect. In addition, solar terms were applied as the smallest unit to more accurately determine the impact of a change in the climate on the outpatient volume. A segmental learning strategy also was used to predict the daily outpatient volume for the time series data. Results: The GP-based prediction could more effectively extract depth information from a finite training sample size and achieve a better performance for predicting daily outpatient visits, with lower root mean square error (RMSE) and higher coefficient of determination (R2) values, than the seasonal autoregressive integrated moving average (SARIMA) model in the time range of holidays and the holiday effect. Conclusion: GP-based model can achieve better prediction performance by overcoming the shortcomings of the SARIMA model. The results can be applied to support decision-making and planning of outpatient clinic resources, to help managers implement periodic scheduling of available resources on the basis of periodic features, and to perform proactive scheduling of additional resources.