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Front Public Health ; 10: 860536, 2022.
Article in English | MEDLINE | ID: covidwho-1776091


Internet of Things (IoT) involves a set of devices that aids in achieving a smart environment. Healthcare systems, which are IoT-oriented, provide monitoring services of patients' data and help take immediate steps in an emergency. Currently, machine learning-based techniques are adopted to ensure security and other non-functional requirements in smart health care systems. However, no attention is given to classifying the non-functional requirements from requirement documents. The manual process of classifying the non-functional requirements from documents is erroneous and laborious. Missing non-functional requirements in the Requirement Engineering (RE) phase results in IoT oriented healthcare system with compromised security and performance. In this research, an experiment is performed where non-functional requirements are classified from the IoT-oriented healthcare system's requirement document. The machine learning algorithms considered for classification are Logistic Regression (LR), Support Vector Machine (SVM), Multinomial Naive Bayes (MNB), K-Nearest Neighbors (KNN), ensemble, Random Forest (RF), and hybrid KNN rule-based machine learning (ML) algorithms. The results show that our novel hybrid KNN rule-based machine learning algorithm outperforms others by showing an average classification accuracy of 75.9% in classifying non-functional requirements from IoT-oriented healthcare requirement documents. This research is not only novel in its concept of using a machine learning approach for classification of non-functional requirements from IoT-oriented healthcare system requirement documents, but it also proposes a novel hybrid KNN-rule based machine learning algorithm for classification with better accuracy. A new dataset is also created for classification purposes, comprising requirements related to IoT-oriented healthcare systems. However, since this dataset is small and consists of only 104 requirements, this might affect the generalizability of the results of this research.

Documentation/standards , Internet of Things , Bayes Theorem , Delivery of Health Care , Humans , Machine Learning
Bull World Health Organ ; 99(8): 546-547, 2021 Aug 01.
Article in English | MEDLINE | ID: covidwho-1344236


Governments worldwide are pressing ahead with COVID-19 passes, despite significant technical, ethical and social obstacles to implementation. Gary Humphreys reports.

COVID-19 Vaccines/administration & dosage , COVID-19/prevention & control , Documentation/standards , Humans , SARS-CoV-2 , Zimbabwe
J Correct Health Care ; 27(2): 89-102, 2021 06.
Article in English | MEDLINE | ID: covidwho-1262061


During a pandemic, basic public health precautions must be taken across settings and populations. However, confinement conditions change what can be done in correctional settings. Correctional nursing (CN) care, like all nursing care, needs to be named and encoded to be recognized and used to generate data that will advance the discipline and maintain standards of care. The Omaha System is a standardized interprofessional terminology that has been used since 1992 to guide and document care. In 2019, a collaboration between the newly formed American Correctional Nurses Association and the Omaha System Community of Practice began a joint effort with other stakeholders aimed at encoding evidence-based pandemic response interventions used in CN. The resulting guidelines are included and illustrated with examples from CN practice.

COVID-19/nursing , Correctional Facilities/standards , Documentation/standards , Nurses/standards , COVID-19/epidemiology , Humans , Nurses/organization & administration , Nursing Care/standards , Practice Guidelines as Topic , SARS-CoV-2 , United States/epidemiology