Evaluation of Completeness, Comparability, Validity, and Timeliness in Cancer Registries: A Scoping Review.

An essential aspect of cancer registration is data quality. Data quality for Cancer Registries has been reviewed in this paper using four main criteria (comparability, validity, timeliness, and completeness). Medline (via PubMed), Scopus, and Web of Science databases were searched for relevant English articles published from inception until December 2022. Each study was analyzed for its characteristics, measurement method, and data quality features. According to the present study, the majority of articles evaluated the completeness feature, and the fewest evaluated the timeliness feature. A completeness rate of 36% to 99.3% and a timeliness rate of 9% to 98.5% were observed. Standardizing metrics and reporting of data quality is necessary to maintain confidence in the usefulness of cancer registries.

Machine Learning Approaches for Detecting Coronary Artery Disease Using Angiography Imaging: A Scoping Review.

This scoping review aims to identify and summarize the current literature on Machine learning (ML) approaches for detecting coronary artery disease (CAD) using angiography imaging. We comprehensively searched several databases and identified 23 studies that met the inclusion criteria. They employed different types of angiography imaging including computed tomography and invasive coronary angiography. Several studies have used deep learning algorithms for image classification and segmentation, and our findings show that various machine learning algorithms, such as convolutional neural networks, different types of U-Net, and hybrid approaches. Studies also varied in the outcomes measured, identifying stenosis, and assessing the severity of CAD. ML approaches can improve the accuracy and efficiency of CAD detection by using angiography. The performance of the algorithms differed depending on the dataset used, algorithm employed, and features selected for analysis. Therefore, there is a need to develop ML tools that can be easily integrated into clinical practice to aid in the diagnosis and management of CAD.

Self-care for coronavirus disease through electronic health technologies: A scoping review.

Background and Aims: Considering the rapid spread and transmission of COVID-19 and its high mortality rate, self-care practices are of special importance during this pandemic to prevent and control the spread of the virus. In this regard, electronic health systems can play a major role in improving self-care practices related to coronavirus disease. This study aimed to review the electronic health technologies used in each of the constituent elements of the self-care (self-care maintenance, self-care monitoring, and self-care management) during the COVID-19 pandemic. Methods: This scoping review was conducted based on Arksey and O'Malley's framework. In this study, the specific keywords related to "electronic health," "self-care," and "COVID-19" were searched on PubMed, Web of Science, Scopus, and Google. Results: Of the 47 articles reviewed, most articles (27 articles) were about self-care monitoring and aimed to monitor the vital signs of patients. The results showed that the use of electronic health tools mainly focuses on training in the control and prevention of coronavirus disease during this pandemic, in the field of self-care maintenance, and medication management, communication, and consultation with healthcare providers, in the field of self-care management. Moreover, the most commonly used electronic health technologies were mobile web applications, smart vital signs monitoring devices, and social networks, respectively. Conclusion: The study findings suggested that the use of electronic health technologies, such as mobile web applications and social networks, can effectively improve self-care practices for coronavirus disease. In addition, such technologies can be applied by health policymakers and disease control and prevention centers to better manage the COVID-19 pandemic.