Predicting hospital outpatient volume using XGBoost: a machine learning approach

作者：Lingling Zhou, Qin Zhu, Qian Chen, Ping Wang, Hao Huang · 发表于：Scientific Reports · 年份：2025 · DOI：10.1038/s41598-025-01265-y · 被引用次数：17 · 研究领域：Machine Learning in Healthcare、Artificial Intelligence in Healthcare、COVID-19 diagnosis using AI

Hospital outpatient volume is influenced by a variety of factors, including environmental conditions and healthcare resource availability. Accurate prediction of outpatient demand can significantly enhance operational efficiency and optimize the allocation of medical resources. This study aims to develop a predictive model for daily hospital outpatient volume using the XGBoost algorithm. Meanwhile, the forecasting performance was compared with that of the Seasonal AutoRegressive Integrated Moving Average with exogenous regressors (SARIMAX) and Random Forest (RF) models. The dataset comprises daily climate data (e.g., temperature, precipitation, PM2.5 levels), historical outpatient volume records, and the number of outpatient specialists available each day. The data range involved spans from January 1, 2014, to October 31, 2024. Data preprocessing involved addressing missing values and encoding categorical variables. Model performance was assessed using three metrics: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) , Mean Absolute Percentage Error (MAPE), and R-squared (R 2 ) metrics. The XGBoost model exhibited superior predictive accuracy compared to both the SARIMAX and RF models, with the lowest MAE, RMSE, MAPE, and the highest R 2 , successfully capturing key relationships between climate factors, resource availability, and outpatient volume. The number of outpatient specialists, temporal variables (such as year, quarter, month, and weekday), meteorological cond...