A Comprehensive Framework for Data-Driven Management of Chronic Pain with Machine Learning and Advanced Analytics

Chronic pain management presents a complex challenge in healthcare, demanding innovative approaches for effective intervention. This research introduces a comprehensive framework leveraging machine learning and advanced analytics to address this challenge. In addition, the platform employs cutting-edge analytics to sift through the ocean of healthcare data in search of knowledge applicable to real-world scenarios. By identifying trends in patients' responses to therapies and disclosing associations between various aspects of lifestyle and pain levels, analytics are crucial for determining the most effective method for treating chronic pain. The proposed methodology integrates predictive modeling, forecasting, and treatment optimization algorithms to personalize chronic pain management strategies. By harnessing patient-specific data, including historical pain records, genetic factors, and lifestyle information, the framework aims to enhance accuracy in predicting pain severity, forecasting future pain episodes, and optimizing treatment plans.