ABSTRACT
In recent years, with the rapid development of Internet and computer technology, network education has developed rapidly. With the rapid development of learning technology, online education has been widely popularized. Especially in 2020, novel coronavirus pneumonia suddenly came into being. Online education based on Internet technology has played a great role in the crisis control period. It has also enriched teaching forms and teaching methods. The blended teaching under online and offline integration has increased the availability of students' learning data. Therefore, more and more scholars begin to pay attention to the research of learning early warning based on educational data mining or learning analysis. However, most early warning studies use traditional machine learning algorithms, and there are still deficiencies in the granularity of data collection, technical implementation mechanism, early warning state recognition and so on. With the success of deep learning in artificial intelligence and other fields, scholars began to study the application of deep learning to solve the problems in the field of learning early warning. Combining variational self-coding (LVAE) and deep neural network, this paper proposes a scheme (LVAEpre) which can solve the problem of unbalanced distribution of educational data sets. This paper determines the weight value of each dimension and index by adjusting the weight parameters of the model, and obtains the threshold value of the early warning line, and empirically tests its effectiveness. Finally, the paper designs a learning early warning model and builds a learning early warning platform based on process data. The results show that the early warning effect is good. The proposal of the learning early warning model based on process data and the application of the learning early warning platform have greatly improved the teaching quality, reduced the risk of students failing to attend the course, and effectively realized the early warning function. The experimental results show that the framework improves the prediction ability of identifying risk learners as soon as possible, timely intervene and guide risk learners, improves learning efficiency, and provides effective guidance strategies for the development of network education. [ FROM AUTHOR] Copyright of Journal of Mechanics in Medicine & Biology is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
INTRODUCTION: The World Health Organization announce that novel coronavirus (COVID-19) is pandemic worldwide on March 11, 2020. In this pandemic, cancer patients are prone to become critically ill after being infected with COVID-19 due to special immune conditions, and cannot effectively benefit from the treatment plan designed for normal people. However, only a few literatures report the differences between cancer patients and normal people after being infected with COVID-19. There is no systematic review to evaluate the clinical, inflammatory, and immune differences between COVID-19 patients with and without cancer. The systematic review aims to summarize and analyze the clinical, inflammatory, and immune differences between them. METHODS AND ANALYSIS: We plan to conduct a systematic review according to the Preferred Reporting Items for Systematic Review and Meta-analysis Protocols (PRISMA-P) guidelines. Several databases (PubMed/MEDLINE, Embase, Web of Science, The Cochrane Library, CNKI, CBM, VIP, WanFang) were searched for relevant eligible observational studies on COVID-19 patients with cancer published from December 2019 to September 2020. Two researchers (Y.ZY and W.PP) will independently complete search strategy formulation, literature selecting, Information extraction, data collation, and quality assessment. The primary outcome will be the clinical characteristics differences between COVID-19 patients with and without cancer. Secondary outcomes will include immune function regulation characteristics such as T cell subset status, inflammation and other factors for COVID-19 patients with cancer. We intend to perform a meta-analysis of studies calculating odds ratio differences (Hedge g) for comparison in Forest plots and subgroup analysis after assessment of heterogeneity using I statistics based on compatibility on the basis of population and outcomes. ETHICS AND DISSEMINATION: We will use the information from published researches with no need for ethical assessment. Our findings will be published in a peer-reviewed journal according to the PRISMA guidelines. PROSPERO REGISTRATION NUMBER: CRD42020204417.