2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Dingkun Li ◽  
Hyun Woo Park ◽  
Erdenebileg Batbaatar ◽  
Lkhagvadorj Munkhdalai ◽  
Ibrahim Musa ◽  
...  

Hadoop is a globally famous framework for big data processing. Data mining (DM) is the key technique for the discovery of the useful information from massive datasets. In our work, we take advantage of both platforms to design a real-time and intelligent mobile health-care system for chronic disease detection based on IoT device data, government-provided public data and user input data. The purpose of our work is the provision of a practical assistant system for self-based patient health care, as well as the design of a complementary system for patient disease diagnosis. This system was only applied to hypertensive disease during the first research stage. Nevertheless, a detailed design, an implementation, a clear overview of the whole system, and a significant guide for further work are provided; the entire step-by-step procedure is depicted. The experiment results show a relatively high accuracy.


2019 ◽  
Vol 124 (8) ◽  
pp. 1165-1170 ◽  
Author(s):  
Hasan Rehman ◽  
Sarah T. Ahmed ◽  
Julia Akeroyd ◽  
Dhruv Mahtta ◽  
Xiaoming Jia ◽  
...  

Author(s):  
P. L. N. Sowjanya

Diabetic retinopathy is one of the prevalent reasons of sight impairment in this day and age According to an epidemiology study, diabetic retinopathy affects one out of every three diabetics. In today's world, disease diagnosis is an essential part of medical imaging. In medical imaging, machine learning gives a greater vision for detecting disease. The objective is to detect diabetic retinopathy using ML. Machine learning in medical imaging could speed up and enhance the detection of sight caused by sugar. In order to detect diabetic retinopathy quickly and support the health-care system, this study will look at several machine learning methodologies, algorithms, and simulations. CNN is used to train the model.


2021 ◽  
Vol 1 (5) ◽  
Author(s):  
Karen B. Born ◽  
Wendy Levinson

In the pandemic era, the Choosing Wisely Canada campaign has taken on new meanings and urgency. There are drops in utilization for necessary health care services and procedures during the pandemic; however, there is growing literature suggesting that some decreases in utilization have been driven by declines in low-value tests and treatments. As resources have shifted to pandemic priorities and essential care needs, these unnecessary tests and treatments have also declined. Choosing Wisely Canada and CADTH are proactively working to highlight key recommendations from evidence-based lists of recommendations to inform priorities for rebuilding, which include avoiding low-value care. Rebuilding the health care system in the post-pandemic era needs to take into account a diversity of perspectives on how to prioritize high-value care for those who need it most, including clinician, patient, and policy expert perspectives.


2018 ◽  
Vol 21 (11) ◽  
pp. 1453-1461 ◽  
Author(s):  
Alana M Rojewski ◽  
Steffani R Bailey ◽  
Steven L Bernstein ◽  
Nina A Cooperman ◽  
Ellen R Gritz ◽  
...  

Abstract The Comorbidity Workgroup of the Tobacco Treatment Research Network, within the Society for Research on Nicotine and Tobacco, previously highlighted the need to provide tobacco treatment to patients diagnosed with comorbid physical and mental health conditions. Yet, systemic barriers in the United States health care system prevent many patients who present for medical treatment from getting the evidence-based tobacco treatment that they need. The identified barriers include insufficient training in the epidemiologic impact of tobacco use, related disorders, and pharmacological and behavioral treatment approaches; misunderstanding among clinicians about the effectiveness of tobacco treatment; lack of therapeutic support from clinical staff; insufficient use of health information technology to improve tobacco use identification and treatment; and limited time and reimbursement for clinicians to provide treatment. We highlight three vignettes demonstrating the complexities of practical barriers at the health care system level. We consider each of the barriers in turn and discuss evidence-based strategies that could be implemented in the clinical care of patients with comorbid conditions. In addition, in the absence of compelling data to guide implementation approaches, we offer suggestions for potential strategies and avenues for future research. Implications: Three vignettes highlighted in this article illustrate some systemic barriers to providing tobacco treatment for patients being treated for comorbid conditions. We explore the barriers to tobacco treatment and offer suggestions for changes in training, health care systems, clinical workflow, and payment systems that could enhance the reach and the quality of tobacco treatment within the US health care system.


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