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Comparison in the strength of numerous non reusable videolaryngoscope cutting blades.
Background Artificial intelligence-based assistive diagnostic systems imitate the deductive reasoning process of a human physician in biomedical disease diagnosis and treatment decision making. While impressive progress in this area has been reported, most of the reported successes are applications of artificial intelligence in Western medicine. The application of artificial intelligence in traditional Chinese medicine has lagged mainly because traditional Chinese medicine practitioners need to perform syndrome differentiation as well as biomedical disease diagnosis before a treatment decision can be made. Syndrome, a concept unique to traditional Chinese medicine, is an abstraction of a variety of signs and symptoms. The fact that the relationship between diseases and syndromes is not one-to-one but rather many-to-many makes it very challenging for a machine to perform syndrome predictions. So far, only a handful of artificial intelligence-based assistive traditional Chinese medicine diagnostic models have bty for generalization. The disease-type prediction accuracies of the top one, top three, and top five were 80.5%, 91.6%, and 94.2%, respectively. Conclusions The main contributions of the artificial intelligence-based traditional Chinese medicine assistive diagnostic system proposed in this paper are that 187 commonly known traditional Chinese medicine diseases can be diagnosed and a novel prediction method called an integrated learning model is demonstrated. This new prediction method outperformed all four existing methods in our preliminary experimental results. With further improvement of the algorithms and the availability of additional electronic health record data, it is expected that a wider range of traditional Chinese medicine disease-types could be diagnosed and that better diagnostic accuracies could be achieved.Background Mobile health (mHealth) apps have the potential to be effective tools for encouraging patients with chronic diseases to self-manage their health. The success of mHealth apps is related to technology acceptance and its subsequent use by intended consumers. Therefore, it is essential to gain insights from consumers' perspectives about their use of mHealth apps in daily life. Objective The purpose of this work was to understand consumers' perspectives on use of a self-management app following completion of a clinical trial that tested the efficacy of the app for improving health outcomes. Methods We conducted five focus groups with paricipants of a clinical trial (NCT03182738) who were randomized to use the video information provider (VIP) for HIV-associated nonAIDS (HANA) conditions app (VIP-HANA) or an attention control app. Thematic analysis was conducted, and the themes were organized according to the two key constructs of the technology acceptance model framework perceived usefulness and perceiveh their clinical care providers. However, only intervention group participants perceived the app to be useful in improving overall health and long-term symptom management. Findings from this study highlight factors that are essential to ensure the usefulness of self-management apps and facilitate sustained use of mHealth apps for people living with chronic illnesses.Background Web-based and mobile interventions to influence physical activity behavior have had limited effects on sustained behavior change. One reason may be that the interventions aim to change largely habitual behavior. Following an identity-oriented approach could be a successful strategy to behavior change because people are committed to behave in line with their self-perception of identity. Objective In this paper, we take a closer look at the role of motivation in long-term adherence to lifestyle interventions. The paper outlines a method for web-based or mobile intervention development that allows exploration of integrating behavior change theory into the design process. We will describe the development of a mobile app that allows people to be self-determined and to value and self-regulate physical activity by adopting an identity-oriented approach. Methods This paper describes a Research through Design (RtD) process in which design activities are carried out as part of the knowledge-generating procesek. In the conceptual design phase, we found that the concept of the mobile app was positively evaluated by participants. However, participants mentioned that terms such as "identity" do not resonate with them and that scenarios could be simpler. Conclusions This paper provides deeper insights into designing for electronic health (eHealth) interventions and services and suggests a new way that motivation can be shaped by the design of an intervention and adherence to physical activity. To the best of our knowledge, this was the first iterative design study in which the effects of adopting an identity approach to both motivation and physical activity were included and observed. Initial promising results were found for using a web-based intervention where habits and identification with the personal importance of a behavior were repetitively triggered.Background Sepsis is a state of organ dysfunction caused by an impaired host response to infection. It is one of the leading causes of death globally. Sepsis, acute myocardial infarction (AMI), and stroke share the primary management requirement of rapid intervention. This could be achieved through early presentation to the hospital, which demands previous knowledge of the disease to ensure better outcomes. Objective Our study aimed to assess the level of public awareness of sepsis compared with AMI and stroke. Methods This was a cross-sectional survey study performed in June and July 2018, with 1354 participants from Jeddah, Saudi Arabia, aged ≥18 years. find more Data entry was performed using Microsoft Excel and statistical analysis including chi-square tests and multilogistic regression was performed using SPSS software. Results A total of 1354 participants were included. Only 56.72% (768/1354) had heard of the term "sepsis" and 48.44% (372/768) of these participants were able to correctly identify it. In addition, 88.
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