5G with Fog Computing based Privacy System in Data Analytics for Healthcare System by AI Techniques

Fog computing architecture is an extended version of the cloud computing architecture to reduce the load of the data transmission and storage in the cloud platform. The architecture of the fog increases the performance with improved efficiency compared with the cloud environment. The fog computing architecture uses the 5G based Artificial Intelligence (AI) technology for performance enhancement. However, due to vast range of data availability privacy is challenging in the fog environment. This paper proposed a Medical Fog Computing Load Scheduling (MFCLS) model for data privacy enhancement. The developed architecture model of optimization-based delay scheduling for task assignment in the fog architecture. The healthcare data were collected and processed with the 5G technology. The developed MFCLS model uses the entropy-based feature selection for the healthcare data. The proposed MFCLS considers the total attributes of 13 for the evaluation of features. With the provision of service level violation, the fog computing network architecture will be provided with reduced energy consumption. The developed load balancing reduced the service violation count with the provision of desired data privacy in the fog model. The estimation of the time frame is minimal for the proposed MFCLS model compared with the existing DAG model. The performance analysis expressed that SLRVM and ECRVM achieved by the proposed MFCLS are 28 and 43 respectively. The comparative examination of the proposed MFCLS model with the existing DAG model expressed that the proposed model exhibits ~6% performance enhancement in the data privacy for the healthcare data

Contemporary use of cefazolin for MSSA infective endocarditis: analysis of a national prospective cohort

Objectives: This study aimed to assess the real use of cefazolin for methicillin-susceptible Staphylococcus aureus (MSSA) infective endocarditis (IE) in the Spanish National Endocarditis Database (GAMES) and to compare it with antistaphylococcal penicillin (ASP). Methods: Prospective cohort study with retrospective analysis of a cohort of MSSA IE treated with cloxacillin and/or cefazolin. Outcomes assessed were relapse; intra-hospital, overall, and endocarditis-related mortality; and adverse events. Risk of renal toxicity with each treatment was evaluated separately. Results: We included 631 IE episodes caused by MSSA treated with cloxacillin and/or cefazolin. Antibiotic treatment was cloxacillin, cefazolin, or both in 537 (85%), 57 (9%), and 37 (6%) episodes, respectively. Patients treated with cefazolin had significantly higher rates of comorbidities (median Charlson Index 7, P <0.01) and previous renal failure (57.9%, P <0.01). Patients treated with cloxacillin presented higher rates of septic shock (25%, P = 0.033) and new-onset or worsening renal failure (47.3%, P = 0.024) with significantly higher rates of in-hospital mortality (38.5%, P = 0.017). One-year IE-related mortality and rate of relapses were similar between treatment groups. None of the treatments were identified as risk or protective factors. Conclusion: Our results suggest that cefazolin is a valuable option for the treatment of MSSA IE, without differences in 1-year mortality or relapses compared with cloxacillin, and might be considered equally effective