Predictive Healthcare Analytics and Remote Patient Monitoring Using Advanced Deep Learning: A Multi-Modal Approach to Precision Medicine

Vijay Kumar; Akhilesh Kumar; Shweta Kumari

doi:10.69980/redvet.v25i2.2096

Vijay Kumar
Akhilesh Kumar
Shweta Kumari

DOI: https://doi.org/10.69980/redvet.v25i2.2096

Keywords: Predictive Analytics, Remote Patient Monitoring, Telemedicine, Deep Learning, Federated Learning, Precision Medicine, Healthcare AI

Abstract

The convergence of predictive analytics, artificial intelligence, and remote patient monitoring (RPM) technologies has created unprecedented opportunities for proactive healthcare delivery. This study presents a comprehensive analysis of multi-modal deep learning approaches for predictive healthcare analytics, focusing on real-time patient monitoring, early disease detection, and personalized treatment optimization. Through examination of 2,847 patients across multiple healthcare institutions, we demonstrate that AI-powered predictive models achieve 97.3% accuracy in predicting adverse health events, reduce hospital readmissions by 38%, and improve patient engagement scores by 67%. Our findings reveal that federated learning architectures enhance privacy preservation while maintaining model performance, with telemedicine integration showing 45% cost reduction and 82% patient satisfaction improvement. The research addresses critical challenges in healthcare accessibility, population health management, and precision medicine through innovative AI-driven solutions.

Author Biographies

Vijay Kumar

Assistant Professor, Department of IT, MIT Muzaffarpur,

Akhilesh Kumar

Assistant Professor, Department of CSE, SIT Sitamarhi,

Shweta Kumari

Assistant Professor, Department of CSE, DCE Darbhanga,

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