scholarly journals Measuring and Preventing COVID-19 Using the SIR Model and Machine Learning in Smart Health Care

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Saad Awadh Alanazi ◽  
M. M. Kamruzzaman ◽  
Madallah Alruwaili ◽  
Nasser Alshammari ◽  
Salman Ali Alqahtani ◽  
...  
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Health Care Services ◽  
Fog Computing ◽  
Well Being ◽  
Reproductive Rate ◽  
Cognitive Computing ◽  
Infected People ◽  
Global Challenge ◽  
Smart Health

COVID-19 presents an urgent global challenge because of its contagious nature, frequently changing characteristics, and the lack of a vaccine or effective medicines. A model for measuring and preventing the continued spread of COVID-19 is urgently required to provide smart health care services. This requires using advanced intelligent computing such as artificial intelligence, machine learning, deep learning, cognitive computing, cloud computing, fog computing, and edge computing. This paper proposes a model for predicting COVID-19 using the SIR and machine learning for smart health care and the well-being of the citizens of KSA. Knowing the number of susceptible, infected, and recovered cases each day is critical for mathematical modeling to be able to identify the behavioral effects of the pandemic. It forecasts the situation for the upcoming 700 days. The proposed system predicts whether COVID-19 will spread in the population or die out in the long run. Mathematical analysis and simulation results are presented here as a means to forecast the progress of the outbreak and its possible end for three types of scenarios: “no actions,” “lockdown,” and “new medicines.” The effect of interventions like lockdown and new medicines is compared with the “no actions” scenario. The lockdown case delays the peak point by decreasing the infection and affects the area equality rule of the infected curves. On the other side, new medicines have a significant impact on infected curve by decreasing the number of infected people about time. Available forecast data on COVID-19 using simulations predict that the highest level of cases might occur between 15 and 30 November 2020. Simulation data suggest that the virus might be fully under control only after June 2021. The reproductive rate shows that measures such as government lockdowns and isolation of individuals are not enough to stop the pandemic. This study recommends that authorities should, as soon as possible, apply a strict long-term containment strategy to reduce the epidemic size successfully.

Hypnotherapy as an Adjunct to Medical Treatment: Highlighting Effectiveness and Identifying Barriers to Further Integrative Treatment

2021 ◽  
pp. 097206342110115
Author(s):  
Feryad A. Hussain
Keyword(s):  
Health Care ◽  
Medical Treatment ◽  
Health Care Services ◽  
Well Being ◽  
Medical Treatments ◽  
Care Services ◽  
Secondary Health Care ◽  
History Of ◽  
Clinical Hypnosis ◽  
The Mind

Integrative models of health care have garnered increasing attention over the years and are currently being employed within acute and secondary health care services to support medical treatments in a range of specialities. Clinical hypnosis has a history of working in partnership with medical treatments quite apart from its psychiatric associations. It aims to mobilise the mind–body connection in order to identify and overcome obstacles to managing symptoms of ill health, resulting in overall improved emotional and physical well-being. This article aims to encourage the use of hypnotherapy in physical health care by highlighting the effectiveness of hypnosis as an adjunct to medical treatment and identifying barriers preventing further integrative treatments.

scholarly journals Socio-demographic profile of child and adolescent users of oral health services in Victoria, Australia

2014 ◽  
Vol 30 (9) ◽  
pp. 1903-1911 ◽  
Author(s):  
J Rodrigo ◽  
Hanny Calache ◽  
Martin Whelan
Keyword(s):  
Health Care ◽  
Oral Health ◽  
Health Services ◽  
Health Care Services ◽  
Social Inequalities ◽  
Secondary Analysis ◽  
Well Being ◽  
Oral Health Care ◽  
Sample Mean ◽  
Oral Health Services

The aim of this study was to investigate the socio-demographic characteristics of the eligible population of users of public oral health care services in the Australian state of Victoria, aged 17 years or younger. The study was conducted as a secondary analysis of data collected from July 2008 to June 2009 for 45,728 young clients of public oral health care. The sample mean age was 8.9 (SD: 3.5) years. The majority (82.7%) was between 6 and 17 years of age, and 50.3% were males. The majority (76.6%) was Australian-born and spoke English at home (89.1%). The overall mean DMFT was 1.0 (SD: 2.1) teeth, with a mean dmft of 3.16 (SD: 5.79) teeth. Data indicate that, among six year olds in the Significant Caries Index (SiC) category, the mean dmft was 6.82 teeth. Findings corroborate social inequalities in oral health outcome and provide suggestions for oral health services to develop strategies and priorities to reduce inequalities in health and well-being, and better coordinate and target services to local needs.

scholarly journals Health-care Workforce Training to Effectively Support Family Caregivers of Seniors in Care

2020 ◽  
Vol 23 (2) ◽  
pp. 160-171
Author(s):  
Rachel Fisher ◽  
Jasneet Parmar ◽  
Wendy Duggleby ◽  
Peter George J. Tian ◽  
Wonita Janzen ◽  
...  
Keyword(s):  
Health Care ◽  
Family Caregivers ◽  
Health Care Professionals ◽  
Health Care Services ◽  
Well Being ◽  
Workforce Training ◽  
Community Dwelling ◽  
Canadian Health Care System ◽  
Care Services ◽  
Qualitative Thematic Analysis

Introduction Family caregivers (FCGs) play an integral, yet often invisible, role in the Canadian health-care system. As the population ages, their presence will become even more essential as they help balance demands on the system and enable community dwelling seniors to remain so for as long as possible. To preserve their own well-being and capacity to provide ongoing care, FCGs require support to the meet the challenges of their daily caregiving responsibilities. Supporting FCGs results in better care provision to community-dwelling seniors receiving health-care services, as well as enhancing the quality of life for FCGs. Although FCGs rely upon health-care professionals (HCPs) to provide them with support and services, there is a paucity of research pertaining to the type of health workforce training (HWFT) that HCPs should receive to address FCG needs. Programs that train HCPs to engage with, empower, and support FCGs are required. Objective To describe and discuss key findings of a caregiver symposium focused on determining components of HWFT that might better enable HCPs to support FCGs. Methods A one-day symposium was held on February 22, 2018 in Edmonton, Alberta, to gather the perspectives of FCGs, HCPs, and stakeholders. Attendees participated in a series of working groups to discuss barriers, facilitators, and recommendations related to HWFT. Proceedings and working group discussions were transcribed, and a qualitative thematic analysis was conducted to identify key themes. Results Participants identified the following topic areas as being essential to training HCPs in the provision of support for FCGs: understanding the FCG role, communicating with FCGs, partnering with FCGs, fostering FCG resilience, navigating healthcare systems and accessing resources, and enhancing the culture and context of care. Conclusions FCGs require more support than is currently being provided by HCPs. Training programs need to specifically address topics identified by participants. These findings will be used to develop HWFT for HCPs.

scholarly journals Identifying Military Veterans in a Clinical Research Database using Natural Language Processing

2019 ◽  
Vol 4 (3) ◽  
Author(s):  
Daniel Leightley ◽  
Katharine M Mark ◽  
David Pernet ◽  
Dominic Murphy ◽  
Nicola T Fear ◽  
...  
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Health Care ◽  
National Health Service ◽  
Mental Health Care ◽  
Health Care Services ◽  
Automatic Machine ◽  
Machine Learning Method ◽  
Learning Method ◽  
Care Services

BackgroundThere is a lack of quantitative evidence concerning United Kingdom veterans who access secondary mental health care. This is mainly due to a person’s veteran status not being routinely collected when they enter the health care system. Main AimThe study aimed to develop an NLP approach for identifying veterans accessing secondary mental health care services using National Health Service electronic health records. MethodsVeterans were identified using the South London and Maudsley Biomedical Research Centre (SLaM) case register – a database holding secondary mental health care electronic records for the South London and Maudsley National Health Service Trust of 300,000 patients. We developed two methods. An NLP and machine learning tool were developed to automatically evaluate personal history statements written by clinicians. ResultsThis study showed that it was possible to identify veterans using the NLP and machine learning approach on a sub-set of 4,200 patients. The automatic machine learning method was able to identify 270 veterans representing an accuracy of 97.2%. It is estimated to take between 6 to 16 minutes to manually search patient history statements whereas the automatic machine learning method took only one minute to run. ConclusionWe have shown that it is possible to identify veterans using NLP combined with machine learning. This work contributes towards the development of a more comprehensive picture of veterans who are accessing secondary mental health care services in the UK. It represents a first step in identifying veterans from one dataset and we hope that future research can inform the possibility of deploying the methods nationally. Despite our success in the current work, the tools are tailored to the SLaM dataset and future work is needed to develop a more agnostic framework. FundingForces in Mind Trust

scholarly journals Rural Michigan Farmers’ Health Concerns and Experiences: A Focus Group Study

2021 ◽  
Vol 12 ◽  
pp. 215013272110535
Author(s):  
Nathan Wright ◽  
Marylee Scherdt ◽  
Michelle L. Aebersold ◽  
Marjorie C. McCullagh ◽  
Barbara R. Medvec ◽  
...  
Keyword(s):  
Health Care ◽  
Focus Groups ◽  
Health Care System ◽  
Health Care Services ◽  
Well Being ◽  
The United States ◽  
Full Time ◽  
Rural Residents ◽  
Care Services ◽  
Care System

Objectives: Rural residents comprise approximately 15% of the United States population. They face challenges in accessing and using a health care system that is not structured to meet their unique needs. It is important to understand rural residents’ perceptions of health and experiences interacting with the health care system to identify gaps in care. Methods: Our team conducted focus groups with members of the Michigan Farm Bureau during their 2019 Annual Meeting. Topics explored included resources to manage health, barriers to virtual health care services, and desired changes to localized healthcare delivery. Surveys were used to capture demographic and internet access information. Conclusion: Analysis included data from 2 focus groups (n = 14). Participants represented a wide age range and a variety of Michigan counties. The majority were full-time farm owners with most—93% (n = 13)—reporting they had access to the internet in their homes and 86% (n = 12) reporting that their cellphones had internet capabilities. Participants identified challenges and opportunities in 4 categories: formal health care; health and well-being supports; health insurance experiences; and virtual health care. Conclusion: The findings from this study provide a useful framework for developing interventions to address the specific needs of rural farming residents. Despite the expressed challenges in access and use of health care services and resources, participants remained hopeful that innovative approaches, such as virtual health platforms, can address existing gaps in care. The study findings should inform the design and evaluation of interventions to address rural health disparities.

A Survey on Health Care Services Using Wireless Sensor Networks

2015 ◽  
pp. 132-151
Author(s):  
Sunilkumar S. Manvi ◽  
Manjula R. B.
Keyword(s):  
Health Care ◽  
Heart Disease ◽  
Health Care Services ◽  
Health Centre ◽  
Ambient Assisted Living ◽  
Well Being ◽  
Wireless Sensor ◽  
Future Research ◽  
Care Services ◽  
The Individual

Although the present technology has aided in development of high-technology-based disease detection machines, potential medicines and devices, the well-being of the individual remains a challenge. Human beings are struggling to control diseases such as Parkinson's disease, Alzheimer's disease, asthma, hypertension, insomnia, heart disease, and diabetes due to non-availability of patient's real-time data for comprehensive study and analysis. Smart health centre environments represent the evolutionary developmental step towards intelligent health care. The Wireless Sensor Network (WSN) with pervasive and ubiquitous computing may be a solution for this predicament. WSNs are a key technology for ambient assisted living. The concept of WSN is used to measure the various health parameters like blood pressure, blood clot, allergy, ECG, cholesterol, RBCs, etc. In this chapter, the authors highlight the importance of WSNs with respect to health care services and discuss some of its challenging applications for diseases like Parkinson's, Alzheimer's, asthma, and heart disease. They delineate the challenges that researchers face in this area that may lead to future research.

Emergency Department Return Prediction System using Blood Samples with LightGBM for Smart Health Care Services

2020 ◽  
pp. 1-1
Author(s):  
Younghwan Shin ◽  
Sangdo Kim ◽  
Jong-Moon Chung ◽  
Hyun Soo Chung ◽  
Sang Gil Han ◽  
...  
Keyword(s):  
Health Care ◽  
Emergency Department ◽  
Health Care Services ◽  
Prediction System ◽  
Blood Samples ◽  
Care Services ◽  
Smart Health

Interstate Level Comparison of People’s Health Status and the State of Public Health Care Services in India

2014 ◽  
Vol 16 (4) ◽  
pp. 489-507 ◽  
Author(s):  
Rajkishor Meher ◽  
Rajendra Prasad Patro
Keyword(s):  
Public Health ◽  
Health Care ◽  
Health Status ◽  
Tamil Nadu ◽  
Health Care Services ◽  
Health Indicators ◽  
Well Being ◽  
Sub Saharan Africa ◽  
Public Health Care ◽  
Care Services

Health is an essential component of economic development and there is a strong correlation between health of human population and societal well-being. We cannot just think of the development of the human capital without the development of health and education of the people. However, it is found that although India has made large gains on the health front of its population, there exist wide variations between and within states. While states such as Kerala, Punjab and Tamil Nadu have a very developed health sector and the health indicators of these states are comparable to those of developed middle-income countries, states such as Bihar, Jharkhand, Madhya Pradesh, Chhattisgarh, Uttar Pradesh, Odisha, etc., are almost at the level of Sub-Saharan Africa. By using a few of the key health indicators the present article makes a critical analysis of the health status of people in the 17 major states of India, the ongoing health development programmes and the present state of public health care services in different parts of the country. The article further delves into an arena of specific policy intervention measures that are required to be undertaken in order to increase the health status of people.

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