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IJERPH, Vol. 19, Pages 12916: Impact of Integrating Machine Learning in Comparative Effectiveness Research of Oral Anticoagulants in Patients with Atrial Fibrillation
Suh We aimed to compare the ability to balance baseline covariates and explore the impact of residual confounding between conventional and machine learning approaches to derive propensity scores (PS). The Health Insurance Review and Assessment Service database (January 2012–September 2019) was used. Patients with atrial fibrillation (AF) who initiated oral anticoagulants during July 2015–September 2018 were included. The outcome of interest was stroke/systemic embolism. To estimate PS, we used a logistic regression model (i.e., a conventional approach) and a generalized boosted model (GBM) w...
Source: International Journal of Environmental Research and Public Health - October 9, 2022 Category: Environmental Health Authors: Sola Han Hae Sun Suh Tags: Article Source Type: research