Complex Network Analysis with Deep Q- Learning for Predicting Patient Flow in Hospital Systems

This study navigates the complexities of patient flow in hospital systems, leveraging the innovative combination of Complex Network Analysis and Deep Q-Learning. Employing the expansive MIMIC dataset, the research aimed to unearth advanced methodologies to optimize hospital operations. The relevance of this endeavour is underscored by the pressing need for efficient healthcare systems, especially in an era of data-driven decision-making. The study's outcomes, marked by improved precision and true positive rates, speak to the prowess of the amalgamated algorithm. By juxtaposing this combined approach against contemporary algorithms, the research unearthed its unparalleled efficiency and adaptability. The overarching implications of these findings pave the way for reshaping clinical decision-making processes, offering a model that is not only statistically superior but promises a future where health care systems can be more streamlined, responsive, and patient-centric.