Objective:To analyze the existing risks in breast milk management at the neonatal department and provide corresponding countermeasures.Methods:22 risk events were identified in 7 risk links in the process of bottle-fe...Objective:To analyze the existing risks in breast milk management at the neonatal department and provide corresponding countermeasures.Methods:22 risk events were identified in 7 risk links in the process of bottle-feeding of breast milk.Hazard Vulnerability Analysis based on the Kaiser model was applied to investigate and evaluate the risk events.Results:High-risk events include breast milk quality inspection,hand hygiene during collection,disinfection of collectors,cold chain management,hand hygiene during the reception,breast milk closed-loop management,and post-collection disposal.Root cause analysis of high-risk events was conducted and breast milk management strategies outside the hospital and within the neonatal department were proposed.Conclusion:Hazard Vulnerability Analysis based on the Kaiser model can identify and assess neonatal breast milk management risks effectively,which helps improve the management of neonatal breast milk.It is conducive to the safe development and promotion of bottle feeding of breast milk for neonates,ensuring the quality of medical services and the safety of children.展开更多
Brain tumor detection and division is a difficult tedious undertaking in clinical image preparation.When it comes to the new technology that enables accurate identification of the mysterious tissues of the brain,magne...Brain tumor detection and division is a difficult tedious undertaking in clinical image preparation.When it comes to the new technology that enables accurate identification of the mysterious tissues of the brain,magnetic resonance imaging(MRI)is a great tool.It is possible to alter the tumor’s size and shape at any time for any number of patients by using the Brain picture.Radiologists have a difficult time sorting and classifying tumors from multiple images.Brain tumors may be accurately detected using a new approach called Nonlinear Teager-Kaiser Iterative Infomax Boost Clustering-Based Image Segmentation(NTKFIBC-IS).Teager-Kaiser filtering is used to reduce noise artifacts and improve the quality of images before they are processed.Different clinical characteristics are then retrieved and analyzed statistically to identify brain tumors.The use of a BraTS2015 database enables the proposed approach to be used for both qualitative and quantitative research.This dataset was used to do experimental evaluations on several metrics such as peak signal-to-noise ratios,illness detection accuracy,and false-positive rates as well as disease detection time as a function of a picture count.This segmentation delivers greater accuracy in detecting brain tumors with minimal time consumption and false-positive rates than current stateof-the-art approaches.展开更多
文摘Objective:To analyze the existing risks in breast milk management at the neonatal department and provide corresponding countermeasures.Methods:22 risk events were identified in 7 risk links in the process of bottle-feeding of breast milk.Hazard Vulnerability Analysis based on the Kaiser model was applied to investigate and evaluate the risk events.Results:High-risk events include breast milk quality inspection,hand hygiene during collection,disinfection of collectors,cold chain management,hand hygiene during the reception,breast milk closed-loop management,and post-collection disposal.Root cause analysis of high-risk events was conducted and breast milk management strategies outside the hospital and within the neonatal department were proposed.Conclusion:Hazard Vulnerability Analysis based on the Kaiser model can identify and assess neonatal breast milk management risks effectively,which helps improve the management of neonatal breast milk.It is conducive to the safe development and promotion of bottle feeding of breast milk for neonates,ensuring the quality of medical services and the safety of children.
文摘Brain tumor detection and division is a difficult tedious undertaking in clinical image preparation.When it comes to the new technology that enables accurate identification of the mysterious tissues of the brain,magnetic resonance imaging(MRI)is a great tool.It is possible to alter the tumor’s size and shape at any time for any number of patients by using the Brain picture.Radiologists have a difficult time sorting and classifying tumors from multiple images.Brain tumors may be accurately detected using a new approach called Nonlinear Teager-Kaiser Iterative Infomax Boost Clustering-Based Image Segmentation(NTKFIBC-IS).Teager-Kaiser filtering is used to reduce noise artifacts and improve the quality of images before they are processed.Different clinical characteristics are then retrieved and analyzed statistically to identify brain tumors.The use of a BraTS2015 database enables the proposed approach to be used for both qualitative and quantitative research.This dataset was used to do experimental evaluations on several metrics such as peak signal-to-noise ratios,illness detection accuracy,and false-positive rates as well as disease detection time as a function of a picture count.This segmentation delivers greater accuracy in detecting brain tumors with minimal time consumption and false-positive rates than current stateof-the-art approaches.
