Multimodal Deep Learning for Early Disease Prediction Using Electronic Health Records, Medical Imaging, and Wearable Data

Abstract

Early disease prediction is important in modern healthcare as it can lower deaths, hospitalizations and healthcare expenditure. Most current artificial intelligence (AI) systems use unimodal learning methods that process data from electronic health records (EHRs), medical images, or wearable sensors stand-alone without any contextual information, as such offering only a limited level of contextual knowledge and less predictive capability under non-trivial clinical conditions. To tackle this challenge, this study aims to develop a multimodal deep learning framework for early heart failure prediction by leveraging heterogeneous healthcare modalities, such as structured EHR data, cardiac medical imaging (CMI) and physiological wearable sensor signal data. The proposed system represents a transformer network architecture for longitudinal EHR representation learning, transformer-based Vision Transformer (ViT) and Convolutional Neural Network (CNN) for imaging feature extraction, and, for modeling sequential wearable data, temporal deep learning networks. Complementary clinical representations are then fused together in a multimodal fashion in order to construct a global representational framework for the prediction. Performance metrics such as accuracy, F1-score, area under the receiver operating characteristic curve (AUC-ROC), sensitivity and specificity are used to evaluate the model on large-scale multimodal healthcare data with more than 120,000 patient records. Results of the experiments show an AUC-ROC of 0.94 for the proposed multimodal approach, which is about 8.7% higher than the best unimodal baseline, thereby significantly enhancing the early-risk stratification and prediction stability. Indeed, Explainable AI techniques improve the interpretability of models and the degrees of clinical trustworthiness. Results show that the multimodal deep learning approach can significantly enhance the potential for predictive intelligence and health management, while helping to establish trustworthy and timely clinical decision support systems tailored for precision medicine applications.