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| http://dx.doi.org/10.1111/j.1468-0009.2008.00518.x | DOI Listing |
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Division of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan.
Background: Sepsis, a critical global health challenge, accounted for approximately 20% of worldwide deaths in 2017. Although the Sequential Organ Failure Assessment (SOFA) score standardizes the diagnosis of organ dysfunction, early sepsis detection remains challenging due to its insidious symptoms. Current diagnostic methods, including clinical assessments and laboratory tests, frequently lack the speed and specificity needed for timely intervention, particularly in vulnerable populations such as older adults, intensive care unit (ICU) patients, and those with compromised immune systems.
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View Article and Find Full Text PDFNeeds Assessment to Guide the Development of a Pediatric Survivorship Program to Promote the Physical and Psychological Well-being of Children After Cancer Treatment.
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Author Affiliations: The School of Nursing, The Hong Kong Polytechnic University (Dr Chung); and The Nethersole School of Nursing, Faculty of Medicine, The Chinese University of Hong Kong (Dr Li), Hong Kong Special Administrative Region, China; School of Nursing, School of Health and Social Care, Edinburgh Napier University, United Kingdom (Dr Ching); Hong Kong Sanatorium and Hospital (Ms Chiu); Paediatric Haematology and Oncology (Ms Mak) and Department of Paediatric and Adolescent Medicine (Dr Chan), Hong Kong Children's Hospital, Hong Kong Special Administrative Region, China.
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Anomaly detection is crucial in areas such as financial fraud identification, cybersecurity defense, and health monitoring, as it directly affects the accuracy and security of decision-making. Existing generative adversarial nets (GANs)-based anomaly detection methods overlook the importance of local density, limiting their effectiveness in detecting anomaly objects in complex data distributions. To address this challenge, we introduce a generative adversarial local density-based anomaly detection (GALD) method, which combines the data distribution modeling capabilities of GANs with local synthetic density analysis.
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