DEEP LEARNING AND PREDICTIVE ANALYTICS FOR PERSONALIZED HEALTHCARE: UNLOCKING EHR INSIGHTS FOR PATIENT-CENTRIC DECISION SUPPORT AND RESOURCE OPTIMIZATION

Abstract

Personalized medicine is rapidly advancing with deep learning and predictive analytics, starting from using electronic health records to improve clinical decision-making. These technologies advance disease prognosis, treatment customization, and management of healthcare resources, taking the sector toward a proactive approach. Though it looks forward to optimizing patient-centric decision support via DL and predictive analytics in improving clinical decision-making, personalization of treatment, health outcomes forecasting, optimal resource allocation, and patient satisfaction, data integration issues and privacy, as well as issues of interpretability are the challenges on the way. This will integrate deep neural networks with predictive models for the analysis of structured and unstructured EHR data for better accuracy using feature engineering, data augmentation, and hyperparameter tuning. The performance evaluation is done on the basis of real-world patient data, thereby leading to significant improvements in prediction reliability, treatment personalization, and efficiency of decision-making over traditional models. Despite implementation challenges, these technologies promise improved treatments, reduced healthcare costs, and better patient outcomes. Future efforts should emphasize broader integration and ethical considerations.