Predicting Late‐onset Alzheimer Disease (LOAD) following COVID‐19 infection by using machine learning applications on real world data from the National Clinical Cohort Collaborative (N3C)

Work is ongoing to develop random forest, neural network, and gradient boosting models to capture relationships and interactions logistic regression might miss, analyze and compare LOAD prediction accuracy across the models, and select additional features based on their importance and relative contributions to a LOAD diagnosis following COVID-19 infection.