With technological advancements, smart health monitoring systems are gaining growing importance and popularity. Today, business trends are changing from physical infrastructure to online services. With the restrictions imposed during COVID-19, medical services have been changed. The concepts of smart homes, smart appliances, and smart medical systems have gained popularity. The Internet of Things (IoT) has revolutionized communication and data collection by incorporating smart sensors for data collection from diverse sources. In addition, it utilizes artificial intelligence (AI) approaches to control a large volume of data for better use, storing, managing, and making decisions. In this research, a health monitoring system based on AI and IoT is designed to deal with the data of heart patients. The system monitors the heart patient's activities, which helps to inform patients about their health status. Moreover, the system can perform disease classification using machine learning models. Experimental results reveal that the proposed system can perform real-time monitoring of patients and classify diseases with higher accuracy.
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http://dx.doi.org/10.3390/s23104580 | DOI Listing |
J Vasc Access
January 2025
Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, Guangzhou, P.R. China.
Background: Central venous access devices (CVAD) are widely used in patient care, providing an essential, reliable pathway for patients to receive chemotherapy, long-term infusions, and nutritional support. However, a system of exercise management has not been developed in patients with CVAD.
Purpose: To evaluate and summarize the evidence for management exercise in patients with CVAD and provide guidance for clinical practice.
Nano Lett
January 2025
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States.
Rapid validation of newly predicted materials through autonomous synthesis requires real-time adaptive control methods that exploit physics knowledge, a capability that is lacking in most systems. Here, we demonstrate an approach to enable real-time control of thin film synthesis by combining optical diagnostics with a Bayesian state estimation method. We developed a physical model for film growth and applied the direct filter (DF) method for real-time estimation of nucleation and growth rates during pulsed laser deposition (PLD).
View Article and Find Full Text PDFBrain Behav Immun Health
February 2025
Department of Health Sciences, Interdisciplinary Research Center of Autoimmune Diseases-IRCAD, University of Eastern Piedmont, 28100, Novara, Italy.
Major Depressive Disorder (MDD) is a widespread psychiatric condition impacting social and occupational functioning, making it a leading cause of disability. The diagnosis of MDD remains clinical, based on the Diagnostic and Statistical Manual of Mental Disorders (DSM)-5 criteria, as biomarkers have not yet been validated for diagnostic purposes or as predictors of treatment response. Traditional treatment strategies often follow a one-size-fits-all approach obtaining suboptimal outcomes for many patients who fail to experience response or recovery.
View Article and Find Full Text PDFHeliyon
January 2025
Department of Electrical Engineering, Feng Chia University, Taichung, 407802, Taiwan.
This study presents an innovative glucose detection platform, featuring a highly sensitive, non-enzymatic glucose sensor. The sensor integrates nickel nanowires and a graphene thin film deposited on the gate region of an extended-gate electric double-layer field-effect transistor (EGEDL-FET). This unique combination of materials and device structure enables superior glucose sensing performance.
View Article and Find Full Text PDFOphthalmol Sci
November 2024
Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colorado.
Objective: Detecting and measuring changes in longitudinal fundus imaging is key to monitoring disease progression in chronic ophthalmic diseases, such as glaucoma and macular degeneration. Clinicians assess changes in disease status by either independently reviewing or manually juxtaposing longitudinally acquired color fundus photos (CFPs). Distinguishing variations in image acquisition due to camera orientation, zoom, and exposure from true disease-related changes can be challenging.
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