REAL-TIME MONITORING AND PREDICTION OF PATIENT OUTCOMES USING AI ALGORITHMS

Authors

VEERAVARAPRASAD PINDI

Abstract

Real-time monitoring and prediction of patient outcomes using AI algorithms represent a significant advancement in healthcare, offering opportunities to enhance clinical decision-making and improve patient care. This survey paper addresses the research problem of integrating AI technologies into healthcare systems to enable proactive monitoring and prediction of patient outcomes across various medical domains. The paper explores how AI-driven systems analyze diverse data sources, including electronic health records (EHRs), medical imaging, and wearable devices, to predict disease progression, anticipate complications, and personalize treatment strategies in real-time. By reviewing current methodologies, including supervised and unsupervised learning, reinforcement learning, and deep learning models such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), the paper discusses advancements in AI algorithms that enhance predictive accuracy and scalability in healthcare applications. Ethical considerations, including patient privacy and regulatory compliance, are also addressed to ensure the ethical implementation of AI technologies in healthcare settings. The survey concludes by highlighting future directions, including the integration of multimodal data and validation through rigorous clinical trials, to further validate and optimize the effectiveness of AI-driven healthcare solutions

Keywords

Real-time monitoring, prediction, patient outcomes, AI algorithms

REAL-TIME MONITORING AND PREDICTION OF PATIENT OUTCOMES USING AI ALGORITHMS. VEERAVARAPRASAD PINDI. 2018. IJIRCT, Volume 4, Issue 1. Pages 1-14. https://www.ijirct.org/viewPaper.php?paperId=2407068