Deep Learning Models for the Diagnosis and Screening of COVID-19: A Systematic Review.

COVID-19, caused by SARS-CoV-2, has been declared as a global pandemic by WHO. Early diagnosis of COVID-19 patients may reduce the impact of coronavirus using modern computational methods like deep learning. Various deep learning models based on CT and chest X-ray images are studied and compared in this study as an alternative solution to reverse transcription-polymerase chain reactions. This study consists of three stages: planning, conduction, and analysis/reporting. In the conduction stage, inclusion and exclusion criteria are applied to the literature searching and identification. Then, we have implemented quality assessment rules, where over 75 scored articles in the literature were included. Finally, in the analysis/reporting stage, all the papers are reviewed and analysed. After the quality assessment of the individual papers, this study adopted 57 articles for the systematic literature review. From these reviews, the critical analysis of each paper, including the represented matrix for the model evaluation, existing contributions, and motivation, has been tracked with suitable illustrations. We have also interpreted several insights of each paper with appropriate annotation. Further, a set of comparisons has been enumerated with suitable discussion. Convolutional neural networks are the most commonly used deep learning architecture for COVID-19 disease classification and identification from X-ray and CT images. Various prior studies did not include data from a hospital setting nor did they consider data preprocessing before training a deep learning model.

What are the important design features of personal health records to improve medication adherence for patients with long-term conditions? A systematic literature review.

OBJECTIVES: This systematic literature review aims to identify important design features of the electronic personal health record (PHR) that may improve medication adherence in the adult population with long-term conditions. DATA SOURCES: PubMed (including MEDLINE), CINAHL, Science Direct (including EMBASE), BioMed Central, ACM digital, Emerald Insight, Google Scholar and Research Gate. METHODS: Studies that were published between 1 January 2002 and 31 May 2018 in English were included if the participants were adults, with at least one long-term condition, were able to self-administer their medication and were treated in primary care settings. The quality of evidence was assessed with the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system and the risk of bias was appraised using the Cochrane risk of bias tool. RESULTS: From a total of 27 studies that matched the inclusion criteria, 12 were excluded due to low quality of evidence, 10 were rated moderate and 5 were rated high quality. All the included studies had low sample size and limited follow-up duration. Thirteen of the included studies found that the use of a PHR has increased medication adherence. The identified design features are reminders, education, personalisation and tailoring, feedback and alerts, gamification, medication management, medical appointment management, diary and self-monitoring, health condition management, set goals, patient's blog and tethered. It was impossible to draw conclusions as to which feature is important to what group of patients and why. The most frequently identified conditions were HIV and diabetes. This review did not identify any papers with negative results. It was not possible to numerically aggregate the PHR effect due to high heterogeneity of the medication adherence measurement, study type, participants and PHRs used. CONCLUSION: Although we found recurrent evidence that PHRs can improve medication adherence, there is little evidence to date to indicate which design features facilitate this process. PROSPERO REGISTRATION NUMBER: CRD42017060542.

Linking Activity Theory with User Centred Design: A Human Computer Interaction Framework for the Design and Evaluation of mHealth Interventions.

mHealth can offer great potential for the self-management of health conditions and facilitating health services. It is therefore imperative that the design of mHealth systems afford optimum efficacy and effectiveness. Involving end users in collaborative decision making is an essential aspect of increasing acceptance of the treatment intervention. Involving users in the design and evaluation of mHealth systems helps to enable a better understanding of the complexity of user needs and how to incorporate this information effectively into the design process. This chapter discusses how Activity Theory can help to provide a theoretical lens for a User Centred Design framework in the design of mHealth systems. A general overview of Activity Theory and User Centred Design are provided, followed by their application in mHealth. Two use cases are provided that demonstrate how Activity Theory has helped provide a broader contextual analysis to a User Centred iterative approach to system design and evaluation.

Elderly, novice users and health information web sites: issues of accessibility and usability.

The Web can provide a quick and easy way to access health information, especially for elderly users. However, these health information sites need to be accessible and usable. In spite of legislation and clear guidelines, there continues to be issues of poor accessibility and usability. Because of an aging population and the likelihood of being more susceptible to age-related impairments such as restricted vision and mobility, the severity of this problem continues to grow. This article presents the results of an exploratory study aimed at assessing the accessibility and usability of three health information Web sites for elderly novice users. The results from the study show that certain aspects of these Web sites make it difficult for elderly people to use them, especially if the users have impairments. Problematic areas are highlighted regarding usability and accessibility, and recommendations are made based on the findings.