Thirty-day Hospital Readmission Prediction Model Based on Common Data Model with Weather and Air Quality Data

Abstract Many epidemiological studies have established an association between environmental exposure and clinical outcome for hospital admissions. However, few studies have explored the impact of environmental factors, such as ambient air pollution and meteorological factors, on hospital readmissions using predictive analysis. In this study, we aimed to develop a model to predict unplanned hospital readmissions within 30 days of discharge based on the common data model considering weather and air quality factors. Moreover, we validated the proposed model externally. We developed and compared the following machine learning methods: decision tree, random forest, AdaBoost, and gradient boosting machine–based models. We performed 10-fold cross-validation for internal validation, and external validation was performed by applying the model to unseen data. The performance of the prediction model was evaluated using the area under the receiver operating characteristic curve. PM10, rainfall, and maximum temperature were the weather and air quality variables that most impacted the model. Among the four machine learning models, the AdaBoost-based model demonstrated the best performance and was the most accurate in predicting the readmission of patients with musculoskeletal diseases. External validation demonstrated that the model based on weather and air quality factors is transportable.