Predictive Multi-Modal Smart Vital Analysis System using Deep Learning for Early Deterioration Warning

Abstract

The present study is designed to overcome the drawbacks of the conventional healthcare monitoring systems, which do not offer real-time, low-latency, and predictive analysis for early patient deterioration. Current systems typically process data in silos, with cloud-based infrastructure, leading to response delays in critical situations. To address these issues, we propose a Predictive Multi-Modal Smart Vital Analysis System (SHVAMS) that combines IoT data collection, multimodal deep learning, and edge/fog computing for proactive and intelligent healthcare monitoring. The study's research design is quantitative and experimental, with the physiological data (heart rate, blood pressure, oxygen saturation, respiratory rate, and temperature) obtained from IoMT devices in various clinical settings. Preprocessing and standardization of a dataset of 1,000 observations and classification into critical, warning and normal classes was performed using pre-defined clinically valid thresholds. The system is based on multimodal deep learning models that are deployed at the edge layer to facilitate low latency processing and real-time decision-making. The results show that the proposed system is able to provide better prediction accuracy and small time delay as compared with traditional approaches which are based on cloud computing, and about 3.7% of the observations are predicted to be critical cases for immediate intervention. The system increases its ability to detect early physiological deterioration and provide timely alerts, aiding proactive clinical decision making. The integration of multimodal AI with edge computing within a single healthcare framework represents a novel and promising approach for scalable and efficient real-time monitoring.