A Longitudinal Study of Alzheimer's disease Treatment Efficacy and Predictors of Cognitive Decline
CWSF · 2026 Disease & Illness Bronze Medal
Overview
Our project focuses on using statistical measures to make comparisons between the different factors that can affect Alzheimer’s disease cognitive trajectories. Firstly, we conducted a meta-analysis in order to compare the effectiveness of treatments using cognitive measures, which involved collecting and filtering through ~400 papers in order to acquire our data. This comparison demonstrated that treatments targeting the symptoms are more effective at enhancing cognitive performance compared to those impacting the root cause, plaques in the brain, allowing doctors and patients to make more informed choices. Next, a machine learning model was used to create groups of similar types of patients in order to analyze which differences between them are associated most with rate of cognitive decline. This comparison resulted in multiple findings, which culminated in a predictive model utilized to recommend certain treatments and showcase a predicted cognitive trajectory over time.
Awards (2)
- Bronze Medal
- Selected for CWSF 2026
Competition history
- CWSF 2026
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