Journal of Biotechnology & Biomaterials
Open Access

Abstract

Statement of the Problem: In applied Biomedical Engineering discipline of human factors analysis is a complex and evolving study in cardiac surgery. Some realistic effort to reduce human error arose with the observational nature of human factors engineering we can take to analyze Risk Theory in cardiac surgery. According to a report from ECRI, here are the 10 riskiest areas which we have to analyze. Infusion errors which may be deadly to Patient. One big issue that can slip through the cracks is “IV free flow”. Secondly, inadequate cleaning of complex reusable instruments which recent rash infections attributed to the reuse of instruments after sanitizing and staff should be regularly reminded of the correct cleaning protocol. Third missed ventilator alarming Robotic Surgery may also be associated with higher costs and additional risks. Risk Analysis in CVD can be monitor through Bayesian Analysis to integrate independent dataset, Bayes factor as a function of SNP in the CHD population. To test the robustness of Bayesian analysis, we examine two tests of the sensitivity, namely to low significance data sets. we exclude the data sets with comparatively small sample sizes, we also exclude large Bayes Factor. Methodology: We use a Bayesian spatial model to estimate CVD mortality by ward, sex and age group for the period of 2012-2018 in Pakistan. The number of deaths in each ward the period-sex-age group was specified using a Poisson model. The Poisson model estimates mortality in each ward.

Biography

E-mail: nrozi@smiu.edu.com