Shin, Hwashin

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

Medication used to treat human illness is one of the greatest developments in human history. In Canada, prescription drugs have been developed and made available to treat a wide variety of illnesses, from infections to heart disease and so on. Records of prescription drug fulfillment at coarse Canadian geographic scales were obtained from Health Canada in order to track the use of these drugs by the Canadian population.

The obtained prescription drug fulfillment records were in a variety of inconsistent formats, including a large selection of years for which only paper tabular records were available (hard copies). In this work, we organize, digitize, proof and synthesize the full available data set of prescription drug records, from paper to final database. Extensive quality control was performed on the data before use. This data was then analyzed for temporal and spatial changes in prescription drug use across Canada from 1990-2013.

In addition, one of major research areas in environmental epidemiological studies is the study of population health risk associated with exposure to ambient air pollution. Prescription drugs can moderate public health risk, by reducing the drug user's physiological symptoms and preventing acute health effects (e.g., strokes, heart attacks, etc.). The cleaned prescription drug data was considered in the context of a common model to examine its influence on the association between air pollution exposure and various health outcomes. Since, prescription drug data were available only at the provincial level, a Bayesian hierarchical model was employed to include the prescription drugs as a covariate at regional level, which were then combined to estimate the association at national level. Although further investigations are required, the study results suggest that the prescription drugs influenced the air pollution related public health risk.

Author Keywords: Data, Error checking, Population health, Prescriptions