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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 The purpose of this study is to develop an efficient prediction model using vital signs and standard medical score systems, which predicts the clinical severity level of the patient in advance based on the quick sequential organ failure assessment (qSOFA) medical score method. Design/methodology/approach To predict the clinical severity level of the patient in advance, the authors have formulated a training dataset that is constructed based on the qSOFA medical score method. Further, along with the multiple vital signs, different standard medical scores and their correlation features are used to build and improve the accuracy of the prediction model. It is made sure that the constructed training set is suitable for the severity level prediction because the formulated dataset has different clusters each corresponding to different severity levels according to qSOFA score. Findings From the experimental result, it is found that the inclusion of the standard medical scores and their correlation along with multiple vital signs improves the accuracy of the clinical severity level prediction model. In addition, the authors showed that the training dataset formulated from the temporal data (which includes vital signs and medical scores) based on the qSOFA medical scoring system has the clusters which correspond to each severity level in qSOFA score. Finally, it is found that RAndom k-labELsets multi-label classification performs better prediction of severity level compared to neural network-based multi-label classification. Originality/value This paper helps in identifying patient' clinical status.

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