PMO 1040 Artificial Intelligence and Machine Learning in Global Public Health
This course aims to introduce Global Public Health (GPH) professionals to different venues of Artificial Intelligence (AI) as a way to develop automated practices in vaccine development, early monitoring of spatial and temporal environmental risks including, not limited to, water, air, and soil quality. Additionally, the course provides GPH professionals with recent advances in disease pathogen detection in different geographic regions using molecular, bioinformatics, and geospatial methods. The goals of the course will be achieved by covering different modules that demonstrate geographic health data acquisition, preparation, and analyses using machine learning techniques with R, Excel, Python, Jupiter Notebooks, and ArcGIS. The topics in the course mainly focus on understanding geographic health in the context of the one-health approach integrated with biological, epidemiological, and genetic variation across environmental gradients and space and time. For example, some diseases such as pulmonary hypertension (PH) and cardiac amyloidosis are commonly misdiagnosed early on, given that their respective symptoms mimic those of other more common diseases. The students will be provided with critical skills using AI algorithms in detecting these diseases early accounting for spatiotemporal, molecular, and clinical variations.
In summary, the course modules emphasize how environmental health, global health professionals, and med students should account for the spatiotemporal, and epidemiological variation in the context of demography and geography of pathogens and human populations.