Predictions of City-based Respiratory Hospital Visits: Developing and Validating a Machine Learning Model with a Novel Composite Air Pollution Index.

OBJECTIVE

City-specific tools for assessing and warning about respiratory disease risks are underdeveloped, limiting effective public health response. This study aimed to develop and validate a novel city-specific prediction framework (WHA air-LSTM) for forecasting daily respiratory outpatient visits by integrating a composite air pollution health index.

METHODS

Based on over 223.7 million hospital visits across multiple megacities, we constructed and validated a five-level morbidity-driven composite air pollution index (WHA air) for each city using city-specific exposure-response relationships. An LSTM model was built using WHA air, temperature, humidity, and historical visit data to predict next-day visits. The proposed modeling framework was developed with city-level data, and it was externally validated using datasets from other cities.

RESULTS

Higher WHA air levels were significantly associated with increased outpatient visits. The model demonstrated excellent predictive performance (Beijing: R 2 = 0.963, RMSE = 53.5) and effectively captured visit surges. Excluding WHA air degraded model accuracy (ΔRMSE = +44.1%). The framework maintained robust performance in external validation, confirming its transferability.

CONCLUSION

The WHA air-LSTM framework provides a scalable and practical tool for city-level respiratory disease early warning by bridging environmental monitoring with clinical practice.