SAS Meets Machine Learning: An Adaptive Framework for Healthcare Data Fusion

Abstract

A Hybrid Approach to Healthcare Data Fusion using SAS and Machine Learning presents a novel framework for integrating traditional SAS-based data management capabilities with modern machine learning algorithms to address the complex challenges of healthcare data integration. This article introduces an adaptive architecture that leverages SAS's robust data processing features alongside specialized machine learning models for entity resolution, missing data imputation, and data quality assessment. This article demonstrates significant improvements in data completeness, accuracy, and consistency compared to traditional methods alone, particularly when handling heterogeneous healthcare data sources, including electronic health records, clinical trials, and medical device outputs. Through a comprehensive article implemented at a major hospital system, this article showcases how this hybrid methodology effectively resolves common integration challenges such as semantic inconsistencies, temporal misalignment, and variable data quality while maintaining regulatory compliance. The proposed framework offers healthcare organizations a scalable, maintainable solution that combines the reliability of established SAS procedures with the adaptability of machine learning techniques, establishing a new paradigm for healthcare data fusion.