Enhancing Predictive Accuracy in Healthcare: A Hybrid Deep Learning Approach Integrating Electronic Health Records and Medical Imaging

Abstract

The integration of Artificial Intelligence (AI) into healthcare holds immense potential for improving diagnostic accuracy, predicting disease progression, and personalizing treatment plans. This paper presents a novel hybrid deep learning approach that leverages both Electronic Health Records (EHRs) and medical imaging data to enhance predictive accuracy in healthcare applications. The proposed model integrates Convolutional Neural Networks (CNNs) for image analysis with Recurrent Neural Networks (RNNs) for sequential data processing from EHRs. We demonstrate the effectiveness of this hybrid architecture through experiments on a dataset comprising patient records and medical images, showing significant improvements in prediction accuracy compared to single-modality approaches and traditional machine learning models. The findings suggest that the synergistic combination of structured and unstructured data provides a more comprehensive patient representation, leading to more accurate and reliable predictive models for healthcare decision-making. We further explore the model's explainability and potential for clinical integration.