![]() These applications dominate industry and research for the development of various smart-world systems. The field of artificial intelligence is involved in Big Data and Analytics, Cloud/Edge Computing-based Big Computing and the Internet of Things (IoT)/Cyber-Physical Systems (CPS) applications. Therefore, there is a need to investigate an automatic method for patients based on vital signs such as hypotensive, heart-rate-disordered and hyperthermal patients and laboratory tests such as albumin and hemoglobin in addition to collecting some medical records such as EEG and EPG signals. This method needs medical examination from the medical expert which is not available all the time. It is a dominant method which is scaled from 0 to 15. Traditionally, the consciousness of a certain patient can be determined based on his eye opening, verbal and motor responses which are the factors of GCS. Level of consciousness can be obtained by evaluation of Glasgow coma scale (GCS) using several methods such as electroencephalography (EEG) and vital signs. ![]() ![]() For this reason, the care givers should rapidly handle the patients in this case to survive them. One of the important medical issues is the detection of level of consciousness which is considered as one of the important observations of the patient. Artificial intelligence is strongly involved in these investigations including machine learning (ML) and deep learning (DL). Therefore, this field attracts researchers to investigate solutions in it. The patients are received in urgent cases in which rapid tests and evaluation of vital signs are very important to obtain an accurate diagnosis and make decisions. The resulting model is accurate, medically intuitive, and trustworthy.Įmergency medicine (EM) is a rapid-growing specialty which is critical and important for the society. The developed ML model is validated using vital signs and laboratory tests extracted from the MIMIC III dataset, and it achieves superior performance (mean absolute error (MAE) = 0.269, mean square error (MSE) = 0.625, R 2 score = 0.964). Moreover, an efficient machine learning (ML) model to predict the level of consciousness of a certain patient based on the patient’s demographic, vital signs, and laboratory tests is proposed, as well as maintaining the explainability issue using Shapley additive explanations (SHAP) that provides natural language explanation in a form that helps the medical expert to understand the final prediction. The main objective of this work is to introduce the edge/cloud system to improve the efficiency of the consciousness measurement through efficient local data processing. Artificial intelligence has been deployed in several applications and appears to have a high performance regarding providing automatic solutions. Therefore, the automatic medical calculation for a patient’s level of consciousness is highly needed. This type of scoring system requires medical examination that may not be available with the shortage of the medical expert. The GCS is a medical score used to describe a patient’s level of consciousness. Among these methods, the automatic estimation of the Glasgow coma scale (GCS) is studied in this paper. Detection of the level of consciousness is one of these observations, which can be detected using several methods. There are many tests and observations are involved in EM. Emergency medicine (EM) is one of the attractive research fields in which researchers investigate their efforts to diagnose and treat unforeseen illnesses or injuries.
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