Clinical Severity Level Prediction Based Optimal Medical Resource Allocation at Mass Casualty Incident

Controlling the mortality rate at Mass Casualty Incident presents an increasingly important challenge for Emergency service organizations. Preparation is required by any healthcare system to minimize loss of life and maximize casualty recovery. The literature conjectures that rates can be decreased integrating technology with emergency management system. To develop a superior rate-controlling plan it critical anticipate clinical condition deterioration casualties effectively serve large number optimal medical resources. Thus, this research proposes multi methodology approach integrates prediction model forecast worsening casualties’ conditions optimization detect resources needed treat casualties. method incorporates Gravitational Search based Back Propagation Neural Network-based together qSOFA score produce accurate casualty’ prediction. findings are then combined methods identify in advance optimum control incoming proposed multi-methodological experimented on MIMIC-II dataset results show allocation supports reduction length. has Accuracy, Sensitivity, Specificity values 91.9%, 94.7%, 83.9% respectively. Further, compared work result shows better performance regard length Queue parameters. involving facilitates clinicians health treatment, thus reducing