A pervasive health care device computing application for brain tumors with machine and deep learning techniques

Purpose Pervasive health-care computing applications in medical field provide better diagnosis of various organs such as brain, spinal card, heart, lungs and so on. The purpose of this study is to find brain tumor diagnosis using Machine learning (ML) and Deep Learning(DL) techniques. The brain diagnosis process is an important task to medical research which is the most prominent step for providing the treatment to patient. Therefore, it is important to have high accuracy of diagnosis rate so that patients easily get treatment from medical consult. There are many earlier investigations on this research work to diagnose brain diseases. Moreover, it is necessary to improve the performance measures using deep and ML approaches. Design/methodology/approach In this paper, various brain disorders diagnosis applications are differentiated through following implemented techniques. These techniques are computed through segment and classify the brain magnetic resonance imaging or computerized tomography images clearly. The adaptive median, convolution neural network, gradient boosting machine learning (GBML) and improved support vector machine health-care applications are the advance methods used to extract the hidden features and providing the medical information for diagnosis. The proposed design is implemented on Python 3.7.8 software for simulation analysis. Findings This research is getting more help for investigators, diagnosis centers and doctors. In each and every model, performance measures are to be taken for estimating the application performance. The measures such as accuracy, sensitivity, recall, F1 score, peak-to-signal noise ratio and correlation coefficient have been estimated using proposed methodology. moreover these metrics are providing high improvement compared to earlier models. Originality/value The implemented deep and ML designs get outperformance the methodologies and proving good application successive score.

Decolonising the HIV Care Cascade: Policy and Funding Recommendations from Indigenous Peoples Living with HIV and AIDS

Indigenous Peoples in settler colonial nations, like Canada, continue to experience the intergenerational trauma, racism, socioeconomic disadvantages, and pervasive health disparities resulting from centuries of systemic oppression. Among these is the disproportionate burden of HIV in Canada’s Indigenous population, coupled with a lack of access to care and services. One method of assessing systems-level gaps is by using the HIV care cascade, whereby individuals are diagnosed, antiretroviral treatment is initiated, and viral suppression is achieved and maintained. The cascade, as it stands today, does not yield positive outcomes for Indigenous Peoples living with HIV. In order to close existing gaps, the authors sought to decolonise the HIV care cascade by rooting it in funding and policy recommendations provided directly by Indigenous Peoples living with HIV. This research presents 29 recommendations that arose when First Nations participants living with HIV partook in traditional storytelling interviews to share their life’s journey and offer suggestions for improving access to care and services. Said recommendations are to localize testing and diagnosis (while upholding confidentiality), improve access to culturally-appropriate care and services, provide targeted programming for Indigenous women and heterosexual men, and increase funding for provincial disability benefits; important steps in decolonising the HIV care cascade.

Acceptance and Potential Impact of the eWALL Platform for Health Monitoring and Promotion in Persons with a Chronic Disease or Age-Related Impairment

Pervasive health technologies can increase the effectiveness of personal health monitoring and training, but more user studies are necessary to understand the interest for these technologies, and how they should be designed and implemented. In the present study, we evaluated eWALL, a user-centered pervasive health technology consisting of a platform that monitors users’ physical and cognitive behavior, providing feedback and motivation via an easy-to-use, touch-based user interface. The eWALL was placed for one month in the home of 48 subjects with a chronic condition (chronic obstructive pulmonary disease—COPD or mild cognitive impairment—MCI) or with an age-related impairment. User acceptance, platform use, and potential clinical effects were evaluated using surveys, data logs, and clinical scales. Although some features of the platform need to be improved before reaching technical maturity and making a difference in patients’ lives, the real-life evaluation of eWALL has shown how some features may influence patients’ intention to use this promising technology. Furthermore, this study made it clear how the free use of different health apps is modulated by the real needs of the patient and by their usefulness in the context of the patient’s clinical status.