The interest in new and more advanced technological solutions is paving the way for the diffusion of innovative and revolutionary applications in healthcare organizations. The application of an artificial intelligence system to medical research has the potential to move toward highly advanced e-Health. This analysis aims to explore the main areas of application of big data in healthcare, as well as the restructuring of the technological infrastructure and the integration of traditional data analytical tools and techniques with an elaborate computational technology that is able to enhance and extract useful information for decision-making. We conducted a literature review using the Scopus database over the period 2010-2020. The article selection process involved five steps: the planning and identification of studies, the evaluation of articles, the extraction of results, the summary, and the dissemination of the audit results. We included 93 documents. Our results suggest that effective and patient-centered care cannot disregard the acquisition, management, and analysis of a huge volume and variety of health data. In this way, an immediate and more effective diagnosis could be possible while maximizing healthcare resources. Deriving the benefits associated with digitization and technological innovation, however, requires the restructuring of traditional operational and strategic processes, and the acquisition of new skills.
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http://dx.doi.org/10.3390/healthcare10071232 | DOI Listing |
Viruses
December 2024
Faculty of Medicine, Federal University of Vale do São Francisco-UNIVASF, Petrolina 56304-917, PE, Brazil.
Arthropod-borne viral diseases are acute febrile illnesses, sometimes with chronic effects, that can be debilitating and even fatal worldwide, affecting particularly vulnerable populations. Indigenous communities face not only the burden of these acute febrile illnesses, but also the cardiovascular complications that are worsened by urbanization. A cross-sectional study was conducted in an Indigenous population in the Northeast Region of Brazil to explore the association between arboviral infections (dengue, chikungunya, and Zika) and cardiac biomarkers, including cardiotrophin 1, growth differentiation factor 15, lactate dehydrogenase B, fatty-acid-binding protein 3, myoglobin, N-terminal pro-B-type natriuretic peptide, cardiac troponin I, big endothelin 1, and creatine kinase-MB, along with clinical and anthropometric factors.
View Article and Find Full Text PDFViruses
November 2024
Center of Excellence in Clinical Virology, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand.
The hepatitis C virus (HCV) infection, a global health concern, can lead to chronic liver disease. The HCV core antigen (HCVcAg), a viral protein essential for replication, offers a cost-effective alternative to HCV RNA testing, particularly in resource-limited settings. This review explores the significance of HCVcAg, a key protein in the hepatitis C virus, examining its structure, function, and role in the viral life cycle.
View Article and Find Full Text PDFSensors (Basel)
December 2024
School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou 450001, China.
In intelligent transportation systems, accurate vehicle target recognition within road scenarios is crucial for achieving intelligent traffic management. Addressing the challenges posed by complex environments and severe vehicle occlusion in such scenarios, this paper proposes a novel vehicle-detection method, YOLO-BOS. First, to bolster the feature-extraction capabilities of the backbone network, we propose a novel Bi-level Routing Spatial Attention (BRSA) mechanism, which selectively filters features based on task requirements and adjusts the importance of spatial locations to more accurately enhance relevant features.
View Article and Find Full Text PDFSensors (Basel)
December 2024
AI and Big Data Department, Endicott College, Woosong University, Daejeon 34606, Republic of Korea.
Sensor networks generate vast amounts of data in real-time, which challenges existing predictive maintenance frameworks due to high latency, energy consumption, and bandwidth requirements. This research addresses these limitations by proposing an edge-cloud hybrid framework, leveraging edge devices for immediate anomaly detection and cloud servers for in-depth failure prediction. A K-Nearest Neighbors (KNNs) model is deployed on edge devices to detect anomalies in real-time, reducing the need for continuous data transfer to the cloud.
View Article and Find Full Text PDFMicroorganisms
December 2024
Department of Radiology, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang 14068, Republic of Korea.
Peritonsillar abscesses and deep neck infection are potentially serious infections among patients with chronic kidney disease (CKD), posing risks for severe complications and drawing significant public health concern. This nationwide, population-based longitudinal study (2002-2019) assessed the extended relationship between chronic kidney disease (CKD) and the likelihood of peritonsillar abscess and deep neck infection in a Korean cohort. Using a 1:4 propensity score overlap-weighted matching, we included 16,879 individuals with CKD and 67,516 comparable controls, accounting for demographic variables and comorbidities to ensure balanced group comparisons.
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