Abstract
This research examines patterns in cancer treatment by analyzing electronic medical record (EMR) data, with the goal of optimizing healthcare provision and improving patient outcomes. The study aims to apply Bayesian prediction models and regression analysis to determine the posterior probability of comorbidities and forecast patient arrivals. The implemented algorithms allow for the customization of treatment techniques, resulting in enhanced effectiveness of therapy and improved decision-making in healthcare delivery. Utilizing Bayesian approaches to analyze EMR data provides insights into the intricacies of cancer treatment and related expenses. The application of this study could be useful to enhance healthcare information systems and informatics by using data-driven insights to improve cancer care practices and operational efficiency in hospital settings.
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