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KalmanFormer: using transformer to model the Kalman Gain in Kalman Filters.
Front Neurorobot
January 2025
Software Engineering College, Zhengzhou University of Light Industry, Zhengzhou, China.
Introduction: Tracking the hidden states of dynamic systems is a fundamental task in signal processing. Recursive Kalman Filters (KF) are widely regarded as an efficient solution for linear and Gaussian systems, offering low computational complexity. However, real-world applications often involve non-linear dynamics, making it challenging for traditional Kalman Filters to achieve accurate state estimation.
View Article and Find Full Text PDFApplication of artificial intelligence in the health management of chronic disease: bibliometric analysis.
Front Med (Lausanne)
January 2025
School of Nursing, Fujian University of Traditional Chinese Medicine, Fuzhou, China.
Background: With the rising global burden of chronic diseases, traditional health management models are encountering significant challenges. The integration of artificial intelligence (AI) into chronic disease management has enhanced patient care efficiency, optimized treatment strategies, and reduced healthcare costs, providing innovative solutions in this field. However, current research remains fragmented and lacks systematic, comprehensive analysis.
View Article and Find Full Text PDFScreening and evaluation of key technologies for non-bioartificial liver care: an empirical study.
Front Med (Lausanne)
January 2025
Department of Infectious Diseases, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Introduction: To identify key technologies within non-bioartificial liver (NBAL, an extracorporeal support system that temporarily replaces some of the liver's functions) nursing to offer guidance for clinical practice. In the context of NBAL nursing, key technologies are crucial for successful implementation of artificial liver treatment, ensuring patient safety, and enhancing nursing quality. A review of both domestic and foreign literature revealed that studies on NBAL nursing technology are lacking and that the key technologies for NBAL nursing have not been clearly identified.
View Article and Find Full Text PDFA streamlined U-Net convolution network for medical image processing.
Quant Imaging Med Surg
January 2025
Department of Information Management, National Yunlin University of Science & Technology, Yunlin.
Background: Image segmentation is crucial in medical diagnosis, helping to identify diseased areas in images for more accurate diagnoses. The U-Net model, a convolutional neural network (CNN) widely used for medical image segmentation, has limitations in extracting global features and handling multi-scale pathological information. This study aims to address these challenges by proposing a novel model that enhances segmentation performance while reducing computational demands.
View Article and Find Full Text PDF[Reducing the Invalid Alarm Rate for Fetal Heartbeat Monitoring in High-Risk Pregnancies in the Delivery Room].
Hu Li Za Zhi
February 2025
Department of Nursing, National Taiwan University Hospital, Taiwan, ROC.
Background & Problems: Fetal heartbeat (FHB) monitoring is used to monitor the condition of a fetus in the uterus and estimate the expected time of delivery. It is crucial to accurately and comprehensively monitor fetal heart beats in high-risk pregnancies. However, invalid alarms frequently occur in clinical environments, easily promoting alarm fatigue among nurses and causing anxiety and distrust in the laboring women and their families.
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