A Multi-Modal Cloud Architectures for Healthcare and Predictive Clinical Analytics

Abstract

The data in healthcare is highly heterogeneous and is continuously increasing from Electronic Health Records (EHRs), medical imaging, wearable Internet of Things (IoT) devices, laboratory systems, and genomic sequencing, which makes it difficult to support intelligent clinical decision making. Current cloud-based healthcare systems may lack sufficient interoperability, scalability, privacy protection, and integration with multiple modalities, due to various factors. The paper proposes a novel multi-modal cloud architecture, which combines edge computing, cloud data lakes, multimodal feature fusion, explainable artificial intelligence (XAI) and federated learning technologies to achieve secure and scalable predictive clinical analytics. The architecture uses deep learning techniques, such as Convolutional Neural Networks, Vision Transformers, Long Short-Term Memory networks, Graph Neural Networks, and multimodal Transformers, to process and analyse diverse healthcare data types. The superior disease prediction accuracy (99.21%), lower latency (42 ms), greater privacy preservation and greater clinical interpretability over existing healthcare frameworks are confirmed by the experimental evaluation performed on benchmark datasets, including MIMIC-IV, eICU, CheXpert, MIMIC-CXR, PhysioNet, BRATS, NIH Chest X-ray and UK Biobank.