Abstract
Machine learning techniques have been recommended as promising alternatives to logistic regression for the estimation of propensity scores. This study compared the performance of the machine learning approach Inverse Probability Weighting- Gradient Boosted Modeling (IPTW-GBM) with traditional Propensity Scores (PS) method in predicting risk of serious events across individual Cholinesterase inhibitors (ChEIs) use in older adults with dementia.
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