A Neural Network based algorithm for assessing risk priority of medical equipments

This paper presents a robust algorithm for the assessment of risk priority for medical equipments based on the calculation of static and dynamic risk factors and Neural Networks (NNs). Four risk parameters have been calculated for a total of 345 different medical devices in two general hospitals in Baghdad. Static risk factor components (equipment function and physical risk) and dynamics risk components (maintenance requirements and risk points) were determined for the medical equipments under consideration. These risk components were used as an input to the feed forward NN trained with Back Propagation algorithm (BPNN). The accuracy of the network was found to be equal to 96% for the proposed system. Hence, this algorithm could serve as promising tool for risk factor assessment for the service departments in large hospitals in Iraq.