Predicting ICU Admissions based on Preconditions and Comorbidity for SARS COV2 Infected Patients

Hospitals across the globe have severe constraints in regard to ICU facilities, beds, and other life support systems. However, in certain situations including natural calamities, epidemics or pandemics, large-scale accidents, and so on, the requirement for ICU beds and resources immediately gets augmented. During such times, there exists an impending need for an optimum apportioning of ICU admissions and resources so that those patients who need critical care are given at the right point of time. The onslaught of COVID-19 pandemic has exuded a high probability of virus transmissions and subsequent complications in patients with co-morbidities and relevant medical issues, resulting in the exploration and investigation of models that could forecast the need for ICU admissions with a higher degree of accuracy. In this research study, a patient's pre-condition dataset will be used that is categorical in nature. Feature selection and extractions are implemented and the modified descriptors are provided as input to the model, for evaluating them based on the metrics namely F1-score, accuracy, specificity, and sensitivity. The prime objective is to build a predictive algorithm that will predict prior to the necessity of ICU admissions based on the patient's comorbidity/ precondition specifically for SARS COV2 infection. © 2022 IEEE.

Research on Prediction of Medical Training Effect Based on PSO-BP Neural Network

Today, the world is still suffering from Coronavirus disease 2019(COVID-19) and other disasters. Therefore, it is critical to improve medical emergency professional training, and ensuring the training effect has become the top priority. As a result, this paper builds a Particle Swarm Optimization Back Propagation(PSO-BP) neural network model using training data from the National Disaster Life Support(NDLS) course to predict NDLS training outcomes. The PSO algorithm is used to calculate the initial weights of the BP network, and the model is then trained using error back propagation to obtain the predicted value of the training effect. When compared to the standard BP neural network prediction results, experimental analysis shows that the prediction model's accuracy reaches 93.24 percentage, and the prediction accuracy is improved by 11.71 percentage. It is also better in terms of convergence speed, minimum error, global search ability, and learning smoothness. This approach is suitable for medical training effect prediction and additionally to assist the training providers in grasping trainees' learning effects in advance to improve training quality. © 2022 IEEE.

A platform for health emergency warning and wandering behaviour detection supporting people with Intellectual Disability

People with intellectual disabilities (ID) encounter several problems in their daily life, regarding the interaction with their environment, the communication with their caregivers and their mobility. Mobile and modern smartwatch applications have great potential to provide early warning for medical emergencies, such as fever, oxygen saturation decline and, recently, Covid-19 disease. Furthermore, such apps can also track their position, if necessary, and even automatically detect wandering behaviour. In this paper, we propose a platform that will be able to a) automatically recognize health emergency situations and/or detection of wandering behaviour based on smartwatch/smartphone sensors, b) provide appropriate notifications to both user and caregiver when needed. The final system is currently developed within a Greek national research project and a pilot implementation will be evaluated by a Greek assistive center for people with ID. © 2022 ACM.

The Application of Controls in Critical Care Ventilation

The COVID-19 pandemic created immense pressure on the global healthcare industry and its supply chains [18] and focused the spotlight on an essential tool in critical care facilities for treating COVID patients: the ventilator. In April of 2020 the Defense Production Act was invoked to accelerate production in the United States to meet both domestic and foreign needs. Ventilators are medical devices intended for patients suffering acute respiratory distress. Rather than providing respiration, which involves gas exchange at the alveolar level, ventilators facilitate the necessary first step in the chain of respiratory life support, getting breathing gas into the lung when the patient is unable to do that on their own. © 2021 IEEE.