AI-Powered Continuous Patient Monitoring: Advancing Healthcare Data Analytics

Abstract

Artificial Intelligence (AI) has revolutionized round-the-clock monitoring of patients since it will enable physiological data to be analyzed in real time using wearable gadgets and smart sensors. This paper suggests a Continuous Patient Monitoring System (CPMS) framework based on AI and integrating the Internet of Medical Things (IoMT), machine learning models, and predictive analytics to improve clinical decision-making. The proposed model uses Support Vector Machine (SVM) and Random Forest (RF) algorithms to detect abnormalities in patients at an early stage and predict their risk. The experimental outcomes on the basis of secondary healthcare datasets show that the SVM model was found to have an accuracy of 94, which surpassed other models in terms of precision and recall. The system guarantees active intercessions, minimizes hospital re-admissions and augments patient safety. The critical discussion is made on comparative evaluation, model optimization and integration issues. Results indicate the possibilities of blockchain to guarantee security of data and 5G-IoMT to provide scalable real-time solutions to healthcare.

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