ANN Assisted-IoT Enabled COVID-19 Patient Monitoring.

COVID-19 is an extremely dangerous disease because of its highly infectious nature. In order to provide a quick and immediate identification of infection, a proper and immediate clinical support is needed. Researchers have proposed various Machine Learning and smart IoT based schemes for categorizing the COVID-19 patients. Artificial Neural Networks (ANN) that are inspired by the biological concept of neurons are generally used in various applications including healthcare systems. The ANN scheme provides a viable solution in the decision making process for managing the healthcare information. This manuscript endeavours to illustrate the applicability and suitability of ANN by categorizing the status of COVID-19 patients' health into infected (IN), uninfected (UI), exposed (EP) and susceptible (ST). In order to do so, Bayesian and back propagation algorithms have been used to generate the results. Further, viterbi algorithm is used to improve the accuracy of the proposed system. The proposed mechanism is validated over various accuracy and classification parameters against conventional Random Tree (RT), Fuzzy C Means (FCM) and REPTree (RPT) methods.

Investigation and analysis of patient factors in identification error events / 中国实用护理杂志

Objective To analyze the characteristics of patients involved in identification error events,try to recognize patients group who had high risk of being wrongly identified.Methods 68 patient identification error events in 64 hospitals in Liaoning province from 2007 to 2011 were investigated.The results were analyzed from four aspects,which were education,age,consciousness and sensory disability state of patients.Results 68 identification error events were investigated.Among these events,patients who graduated from middle school or less constituted 79.41％ ;patients older than 60 years old constituted 55.88％;patients with hearing and speaking inability constituted 41.18％;patients without clear consciousness constitutes 14.70％ Conclusions Patients who graduated from middle school or less,older than 60 years old,with heating and speaking inability constitute the group who has high risk of being wrongly identified.Enhancing the education of patients,promoting the use of wrist band,and decreasing the dependence on hearing and speaking ability during identification process constitute the main reformation aspect of new patient identification rules.