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Leveraging Data in Public Health Research: AI-Driven Polypharmacy Risk Prediction in the Elderly and Genetic Insights into Parkinson’s Disease
This thesis investigates key aspects of aging and neurogenetics through two data-driven projects that emphasize inclusivity, equity, and global collaboration in health research. The first part examines patterns of polypharmacy among older adults using longitudinal data from a pan-European harmonized...
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| Format: | Thesis |
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AUC Knowledge Fountain
2025
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| Summary: | This thesis investigates key aspects of aging and neurogenetics through two data-driven projects that emphasize inclusivity, equity, and global collaboration in health research. The first part examines patterns of polypharmacy among older adults using longitudinal data from a pan-European harmonized dataset encompassing 18 countries. By identifying the predictors of polypharmacy and the utilization of Machine Learning techniques, this work aims to facilitate early polypharmacy risk prediction and inform timely clinical interventions, ultimately, improving outcomes in aging populations.
The second part is situated within the Global Parkinson’s Genetics Program (GP2) and the International Parkinson’s Disease Genomics Consortium (IPDGC) efforts to address disparities in global genetic research by including populations that are historically underrepresented in genomic studies. This work delves into the distribution and potential association of Apolipoprotein E (APOE) alleles with Parkinson’s disease (PD). Further, it explores rare genetic variants, and reveals the prevalence of several known pathogenic mutations in a cohort of Egyptians with PD.
Taken together, this thesis demonstrates how harmonized datasets and contributions to international genomic research efforts can be leveraged to address critical knowledge gaps in aging and neurogenetics. It underscores the importance of accessible, high-quality data and inclusive research practices in improving the quality and reach of scientific research. |
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