چکیده مقاله
In this innovative study, researchers investigated the effectiveness of two dose COVID 19 vaccination in reducing hospitalization amidst the complex confounding factors present in observational studies Propensity scores have become increasingly popular for adjusting confounding variables in such studies While propensity score methods offer theoretical advantages over traditional covariate adjustment methods, their performance in real world situations remains poorly understood By employing Subsequent analysis revealed a significant balance between the vaccinated and unvaccinated groups The results obtained from both Multiple Logistic Regression and Propensity Score Matching methods indicated that vaccinated individuals were less likely to be hospitalized adjusted odds ratio OR , 95% CI using logistic regression: 0 21 0 19, 0 30 , and estimated by propensity score matching using logistic regression and GBM respectively: 0 72 0 70, 0 74 and 0 93 0 91,0 95 These findings not only emphasize the effectiveness of vaccination but also underscore the need for a meticulous approach when assessing real world impacts in complex data environments
کلیدواژهها
نویسندگان
Mahboobeh Taherizadeh
Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran
Mohammad Taghi Shakeri
Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran
Saeed Akhlaghi
Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran
شیوه ارجاع
Taherizadeh, Mahboobeh and Shakeri, Mohammad Taghi and Akhlaghi, Saeed,1403,Estimating Causal Effect of Two-Dose COVID-19 Vaccination on Hospitalization with Machine Learning Techniques: A Propensity Score Matching Approach,1st International Biennial Conference of Artificial Intelligence and Data Science 2024,Bushehr
ارائهشده در
مجموعه مقالات اولین کنفرانس بین المللی دوسالانه هوش مصنوعی و علوم داده3 اردیبهشت 1403 · بوشهر