ArticleScientific reports2025
Predicting hospital outpatient volume using XGBoost: a machine learning approach.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
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- High-Throughput Screening and Interpretable Machine Learning for Rational Design of Bimetallic Catalysts for Methane Activation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Machine Learning-Based Prediction of Institutional Delivery Dropout (IDD) Among Nigerian Women: An Exploratory Study Using SHAP Interpretability.Journal of epidemiology and global health · 2026Article
- Interpretable Machine Learning Model for Survival Prediction in Pediatric Adrenocortical Tumors.Journal of the Endocrine Society · 2026Article
- Domain-Aware Interpretable Machine Learning Model for Predicting Postoperative Hospital Length of Stay from Perioperative Data: A Retrospective Observational Cohort Study.Bioengineering (Basel, Switzerland) · 2026Article
- Dynamic trajectories of inflammatory biomarkers and post-stroke cognitive impairment: a comprehensive review of neuroimmune mechanisms, longitudinal modeling, and clinical translation.Frontiers in neurology · 2026Review
- Optimize a chain convenience store location prediction model by using MTS-machine learning methodology.Scientific reports · 2025Article
- An Interpretable Machine Learning Framework for Analyzing the Interaction Between Cardiorespiratory Diseases and Meteo-Pollutant Sensor Data.Sensors (Basel, Switzerland) · 2025Article
- Leveraging Explainable AI to Identify Determinants of Lifetime HIV Testing Among Adults in Tennessee, United States: Evidence for Targeted Public Health Strategies From BRFSS 2023.Journal of primary care & community healthArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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
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