Development of Models for Air Pollution-Related Public Health Assessment: Application of Long Short-Term Memory Neural Network for Short-term Exposure Effect

This thesis develops an Long Short-Term Memory (LSTM) neural network model to assess the relationship between ambient air pollutant exposure and public health risks, accommodating both linear and nonlinear associations with distributed lags.The research makes three key contributions. First, Maximal Information Coefficient (MIC) methods identify the most relevant air pollutants and their associations with health outcomes. Second, an LSTM model extracts temporally dependent features from exposure series to estimate health impacts. Finally, the model's potential in air pollution epidemiology is explored using Local Interpretable Model-Agnostic Explanations (LIME) to interpret the exposure-health response relationship.

Author Keywords: air pollution epidemiology, Deep Learning, explainable AI, Long Short-Term Memory, Maximal Information Coefficient, public health assessmen