AI-Driven Predictive Analytics in Patient Outcome Forecasting for Critical Care
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Abstract
Artificial Intelligence (AI) has increasingly become a cornerstone of healthcare, especially in the critical care environment, where timely and accurate predictions can significantly impact patient outcomes. Predictive analytics powered by AI models, such as machine learning (ML) and deep learning (DL), offer transformative potential for forecasting critical patient outcomes. This systematic review examines the current state of AI-driven predictive analytics applied to patient outcome forecasting in critical care settings. By synthesizing evidence from various studies, we analyze AI models’ performance, including their accuracy, interpretability, and integration into clinical workflows. The review highlights the range of AI methods—such as logistic regression, support vector machines (SVMs), random forests, and neural networks—employed for predicting conditions such as sepsis, organ failure, mortality risk, and recovery outcomes. It also identifies challenges faced by AI models, including data quality issues, model transparency, and the clinical adoption of these technologies. Finally, the review discusses the future directions of AI in critical care, emphasizing the importance of personalized healthcare, real-time monitoring, and the integration of multi-modal data sources for improving prediction accuracy and patient management.
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