Similar Publications

Integration of artificial intelligence (AI) into radiology practice can create opportunities to improve diagnostic accuracy, workflow efficiency, and patient outcomes. Integration demands the ability to seamlessly incorporate AI-derived measurements into radiology reports. Common data elements (CDEs) define standardized, interoperable units of information.

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Background: Active surveillance (AS) is the guideline-recommended treatment for low-risk prostate cancer and involves routine provider visits, lab tests, imaging, and prostate biopsies. Despite good uptake, adherence to AS, in terms of receiving recommended follow-up testing and remaining on AS in the absence of evidence of cancer progression, remains challenging.

Objective: We sought to better understand urologist, primary care providers (PCPs), and patient experiences with AS care delivery to identify opportunities to improve adherence.

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Multi task opinion enhanced hybrid BERT model for mental health analysis.

Sci Rep

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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Background: Maintaining the physical and psychological well-being of healthcare workers (HCWs) is crucial for health system resilience. In sub-Saharan Africa, particularly Uganda, HCWs faced significant challenges during the coronavirus disease 2019 (COVID-19) pandemic, compounded by pre-existing resource constraints. This study investigated challenges faced by HCWs at a designated COVID-19 hospital ('the Hospital') and explored determinants of maintaining healthcare personnel's motivation during the COVID-19 pandemic in Uganda.

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UAV selection for high-speed train communication using OTFS modulation.

Sci Rep

January 2025

Computational Learning Theory Team, RIKEN-Advanced Intelligence Project, Fukuoka, 819-0395, Japan.

Providing continuous wireless connectivity for high-speed trains (HSTs) is challenging due to their high speeds, making installing numerous ground base stations (BSs) along the HST route an expensive solution, particularly in rural and wilderness areas. This paper proposes using multiple unmanned aerial vehicles (UAVs) to deliver high data rate wireless connectivity for HSTs, taking advantage of their ability to fly, hover, and maneuver at low altitudes. However, autonomously selecting the optimal UAV by the HST is challenging.

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