The Intersection of Data Engineering and AI in Healthcare: A Framework for Predictive Analytics

Abstract

The intersection of data engineering and artificial intelligence (AI) represents a transformative approach in healthcare, enabling more accurate predictive analytics and enhancing patient care. This paper proposes a comprehensive framework integrating data engineering principles with AI techniques to optimize predictive analytics in healthcare settings. The framework addresses key challenges such as data integration, quality, and scalability while leveraging AI methodologies for predictive modeling and decision support. By employing advanced data preprocessing, feature engineering, and model training strategies, the framework aims to improve prediction accuracy for various healthcare outcomes, including patient readmission, disease progression, and treatment efficacy. The proposed framework is evaluated through case studies involving electronic health records (EHRs) and patient data, demonstrating its effectiveness in real-world scenarios. The findings highlight significant improvements in predictive performance and offer actionable insights for healthcare practitioners and researchers. This work contributes to the advancement of healthcare analytics by bridging the gap between data engineering and AI, providing a robust foundation for developing intelligent healthcare solutions.