REAL-TIME MONITORING AND PREDICTION OF PATIENT OUTCOMES USING AI ALGORITHMS
Author(s): VEERAVARAPRASAD PINDI
Publication #: 2407068
Date of Publication: 06.01.2018
Country: India
Pages: 1-14
Published In: Volume 4 Issue 1 January-2018
DOI: https://doi.org/https://doi.org/10.5281/zenodo.12805354
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
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