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| Open Access | THE ROLE OF EMPIRICAL BAYES IN PREDICTING APNEA EPISODES IN SLEEP APNEA PATIENTS
Aiman Bakar , School of Mathematical Science, Universiti Sains Malaysia, MalaysiaAbstract
Sleep apnea is a prevalent and often underdiagnosed condition, with patients experiencing repeated episodes of apnea during sleep. Accurate prediction of these episodes is crucial for effective diagnosis, treatment, and management. This study explores the application of the Empirical Bayes (EB) method to predict the occurrence of apnea episodes in individuals diagnosed with sleep apnea. Using a dataset of clinical sleep study data, the Empirical Bayes approach was employed to estimate the probability of apnea occurrences, integrating prior information and observed data to refine predictions. The results demonstrate that the EB method provides more precise and reliable predictions compared to traditional statistical models, especially in scenarios with sparse or incomplete data. By incorporating both population-level and individual-level information, the EB method offers a valuable tool for clinicians seeking to optimize treatment plans and improve patient outcomes. This study highlights the potential of advanced statistical methods in enhancing our understanding and management of sleep apnea.
Keywords
Empirical Bayes, Sleep Apnea, Apnea Episodes
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Copyright (c) 2024 Aiman Bakar
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