This study aimed to investigate computer and internet access and education attained in patients with chronic obstructive pulmonary disease (COPD) as potential barriers to implementation of telemedicine. We prospectively assessed 98 patients admitted with an acute exacerbation of COPD (mean age: 70.5 ± 9.3 years; force expired volume in the first second: 0.75 ± 0.39 L; 59% male) recording educational level attained and home computer and internet access. Hospital readmission surveillance occurred up to 2.7 (2.6-2.8) years following the index hospital admission. Only 16% of patients had a computer and only 14% had internet access; this group were younger and more educated than those without a computer. There was no difference in hospital readmissions over 2 years between those with and without access to a computer or internet. Only 12% of the whole cohort were educated to a school leaving age of 16 years and this group were more likely to be still working. School leaving age was directly associated with fewer hospital readmissions ( r = 0.251, p = 0.031). In conclusion, these data highlight the current challenges to the widespread implementation of telehealth in COPD patients as there is limited availability of computer and internet access with such patients demonstrating a lower level of education achievement.
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http://dx.doi.org/10.1177/1479972317707653 | DOI Listing |
Purpose: This brief report aims to summarize and discuss the methodologies of eXplainable Artificial Intelligence (XAI) and their potential applications in surgery.
Methods: We briefly introduce explainability methods, including global and individual explanatory features, methods for imaging data and time series, as well as similarity classification, and unraveled rules and laws.
Results: Given the increasing interest in artificial intelligence within the surgical field, we emphasize the critical importance of transparency and interpretability in the outputs of applied models.
BMC Health Serv Res
January 2025
China-ASEAN Institute of Statistics, Guangxi University of Finance and Economics, Mingxiu West Road 100, Nanning, 530003, China.
Background: The popularization of the Internet and digital technology has called for higher digital literacy among citizens, especially the elderly. However, most existing studies didn't measure digital literacy at the micro level, and the impact mechanism has rarely been discussed. The purpose of this study is to clarify whether and how digital literacy affects the health status of senior citizens.
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January 2025
Department of Artificial Intelligence and Data Science, College of Computer Science and Engineering, University of Hail, Hail, Saudi Arabia.
In the present digital scenario, the explosion of Internet of Things (IoT) devices makes massive volumes of high-dimensional data, presenting significant data and privacy security challenges. As IoT networks enlarge, certifying sensitive data privacy while still employing data analytics authority is vital. In the period of big data, statistical learning has seen fast progressions in methodological practical and innovation applications.
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January 2025
Department of Computer Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran.
According to recent research, with the ever-increasing use of Internet of Things (IoT) devices, there has arisen an ever-growing need for high-performance yet low-power circuits that can efficiently process information. Quantum-dot Cellular Automata (QCA) has emerged as a promising alternative to conventional complementary metal-oxide-semiconductor (CMOS) technology due to its great potential in digital design at nanoscale levels on account of very low power consumption and very high processing speed. However, QCA circuits are inherently prone to faults due to variations in manufacturing processes and due to the influence of environmental factors.
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January 2025
Department of Computer Science, College of Computer and Information Sciences, King Saud University, 11543, Riyadh, Saudi Arabia.
Understanding the nuanced emotions and points of view included in user-generated content remains challenging, even though text data analysis for mental health is a crucial instrument for assessing emotional well-being. Most current models neglect the significance of integrating viewpoints in comprehending mental health in favor of single-task learning. To offer a more thorough knowledge of mental health, in this study, we present an Opinion-Enhanced Hybrid BERT Model (Opinion-BERT), built to handle multi-task learning for simultaneous sentiment and status categorization.
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