Evolutionary game theoretic approach with deep learning for health decision-making in critical environment

In critical healthcare environments, timely and informed decision-making is paramount to patient well-being and outcomes. This research introduces an innovative approach that combines evolutionary game theory with deep learning techniques to revolutionize health decision-making in critical settings. Healthcare systems often operate under conditions of uncertainty, with rapidly evolving patient conditions and resource constraints. The proposed approach leverages evolutionary game theory to model the dynamic interactions among healthcare providers, patients, and treatment strategies. This involved a modeling framework, which allows for exploring strategic decisions that adapt to changing conditions. Deep learning, a subfield of artificial intelligence, is integrated into the approach to enhance decision support. Deep neural networks process and analyze vast medical data, including patient records, clinical guidelines, and treatment outcomes. These networks enable the extraction of valuable insights that inform decision-making in real time. One of the key strengths of this research lies in its ability to address critical healthcare challenges, such as resource allocation, treatment selection, and patient prioritization. The evolutionary game theoretic approach enables the modeling of competitive and cooperative interactions among healthcare providers, fostering more effective resource allocation and coordination.