Predictive analytics for patient adherence : A machine learning approach in medication management

Abstract

In this paper, we investigate the use of predictive analytics and machine learning to improve patients’ adherence to prescribed medications. By analyzing past patient data, we are able to create a model that may foresee adherence behaviors based on characteristics like age, gender, health status, and income. In order to analyze and identify major drivers of non-adherence and enable personalized therapies, our method incorporates cutting-edge machine learning techniques. The methodology takes into account each individual’s situation while making suggestions, leading to a more proactive and specific strategy for improving drug adherence. Predictive analytics allows healthcare providers to foresee adherence issues, intervene at the right time, and boost patient outcomes. This research contributes to the developing landscape of healthcare by utilizing the potential of machine learning for precision medicine in drug management, addressing a vital part of patient care and public health.