Artificial Neural Networks In Prevention Of Nosocomials Infections

Abstract These In terms of medical safety the parameters affect nosocomial infections are characterized by their complexity. They become less amenable to direct mathematical modeling based on physical laws since they may be distributed stochastic non-linear and time-varying uncertain etc. The purpose of our study is to develop a predictive model of prevention these diseases. Like the data involved in the growth bacteria process occur in an uncertain environment due to their complexity it becomes necessary to have a suitable methodology for the analysis of these variables. The basic principles of artificial neural networks perfectly suited to this process. As input variables we consider the respiratory metabolism temperature water activity sensitivity and resistance to antibiotics. The bacterial genus is formulated and applied using MATLAB simulation for the system. The result output variable is the bacterial genus planned. It becomes possible to predict bacterial genus capable to proliferate in these conditions. Therefore this will take the necessary decisions as a precautionary.