For critical care environments, clinical outcomes will greatly benefit from predicting patient responses to treatment in time. As such, this research is a proposal of an AI - based physiological modeling framework that utilizes simulation (digital twin) technology, MARL, and self supervised transformers to increase prediction accuracy in treatment response More importantly, this system is rivaling the conventional AI model that does not use the static dataset, but dynamically learning based on the real-time physiological signals, electronic health records and multi-modal medical data, to provide personalized treatment. Digital twin based simulation of individual patient physiology to replicate individual patient physiology makes it possible for AI agents to model organ interaction and infer the potential complications. Using MARL, the treatment strategies are optimised in real time, and XAI techniques guarantee transparency of the decision making, which is crucial to achieve clinical trust. Finally, federated learning improves the flexibility of data learning at many hospitals and preserves data privacy. The proposed framework is applied on real-life ICU datasets and shown to be more accurate, adaptable, and reliable compared to existing methods. Real-time inference at the edge can transform critical care decision making, reduce mortality rate and treatment risks. There is future work dealing with blockchain application integrated for the secure patient data exchange and bio-digital twinning for subsequently advanced predictive modeling to lead to next generation AI enabled critical care systems.
Volume 12 | Issue 5
Pages: 627-633