Triage is the most important requirement of Mass Casualty Incident (MCI) where monitoring the vital signs of casualty is the crucial aspect to assess the severity of their current medical conditions. One of the most significant challenges in the triage center is to provide effective remote monitoring of vital signs of mass casualties. To overcome this challenge, there is a necessity to design a dynamic routing protocol that supports data-centric quality parameters such as delay and reliability as well network-specific quality parameters such as throughput and network lifetime over an ad hoc network. The proposed protocol handles data-centric quality parameters by jointly considering the link and node cost of the neighboring nodes. Further, the protocol handles network-specific quality parameters by including load distribution along with buffer management based on the medical condition of the casualties and beaconless routing mechanism. Furthermore, the proposed approach focuses on the transmission of vital signs of critical casualties while also avoiding network congestion and extending network lifespan. The experimentation results show that the proposed protocol is efficient in handling end-to-end delay, the packet transmission ratio of the critical casualties vital signs as compared to the existing state-of-the-art approaches.

Purpose: Unexpected events, whether man-made or natural, cause significant distress and disorder. The number and magnitude of disasters and catastrophes have been rising globally in recent decades, according to historical data. Continued real-time monitoring of mass casualty along with the arrangement of needed medical resources to handle the mass casualty is required to reduce the mortality and morbidity rates. Methods: Electronic tag-based casualty monitoring and machine learning-based Decision Support Systems are emerging as a more effective and proactive solution that provides continuous remote monitoring of patients. A novel framework based on Body-to-Body Network, Prediction model, and Genetic Algorithm-based medical resource optimization is proposed for the continuous monitoring of the mass casualty and medical resource allocation at the incident. The aim of this work is to give priority to the handling of critical casualties. Firstly, a Quality of Service and load-sensitive routing protocol for transmitting mass casualties' physiological parameters across a wireless network is proposed, with the critical casualty being emphasized. Secondly, the clinical seriousness degree of the mass casualty is predicted using Backpropagation Artificial Neural Network. Finally, an optimization model using a Genetic algorithm and queuing theory is proposed to find the required optimal number of medical resources to handle critical and non-critical casualties separately. Also, the proposed optimization model considers the predicted clinical level transition rates of the mass casualty. Results: The performance and accuracy of the proposed framework are evaluated using the MIMIC-II dataset. The outcome demonstrates that the framework emphasizes critical casualty management. Furthermore, the framework allocates an adequate number of servers by incorporating the proposed routing protocol in comparison to the AODV protocol. Conclusion: The inclusion of a prediction model in the framework aids in allocating an adequate number of servers by considering the predicted clinical deteriorating transition rates of casualties at a mass casualty incident. In terms of the estimated length of the casualty at the incident, the results suggest that incorporating a medical resource optimization model outperforms the non-optimal option.

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

Epilepsy is a chronic neurological disorder characterized by the occurrence of paroxysmal recurrent seizures, which are caused by abnormal electrical activity in the brain. Seizures vary widely in their presentation, depending on the specific region of the brain involved and the extent of the abnormal electrical discharges. The disease can affect cognitive function posing a serious threat to the patients’ lives. Epilepsy causes emotional and behavioral changes, along with sleep disorders and migraines, leading to social isolation and discrimination. Timely administration of medication can cure most cases of epilepsy. However, identifying epileptic patients requires reviewing multiple EEG signal sheets, which can delay disease prediction. Therefore, the aim of our study is to apply simplistic machine learning algorithms that can study the EEG signal data swiftly to identify individuals in seizure, inter-ictal, and normal states, thereby aiding in medical diagnosis. The novelty of this study lies in the utilization of pre-built methods and develop a fast and efficient model that is lightweight and easy to integrate in healthcare to provide relief to epileptic patients. While previous studies have achieved high accuracy, the discussion involving time complexity of their models has been scarce. Given the importance of timely medication in managing epilepsy, it is crucial to consider the runtime of the model rather than solely focusing on accuracy. Therefore, a model that balances both a short runtime (2.9 min) and a satisfactory accuracy (97.46%) has been developed in this project. Integration of this project's findings will catalyze transformative changes within the healthcare industry, enabling healthcare professionals to detect epilepsy at earlier stages and provide timely interventions, ultimately fostering a system that prioritizes precision, innovation, and improved patient outcomes.