Dynamic Dempster Multi-Layer Perceptron for the prediction of admission patient in emergency department

The early identification of the patients’ hospitalization at triage level within the Emergency Department (ED) presents a potential solution to reduce the risk of overcrowding and improve the quality of care. Thus, predicting patient out-come on arrival assists medical staff in the make of the appropriate patient pathway decision and so reduces the risk of medical error and complication of the patient’s condition. Previous works don’t consider the uncertainty of medical data while the management of this uncertainty is one of the most important and crucial tasks of medical information systems. Thus, we present in this paper an improved version of the classical prediction model by taking into account the uncertainty and by managing properly the missing information. In this context, we propose a new approach based on Dempster-Shafer theory and Dynamic Multi-Layer Perceptron algorithm. Our proposed approach deploys the correspondent neural network as follows: 1) computes for each input parameter the Basic Belief Assignment (BBA) that provides an assessment of the uncertainty pattern using the Dempster’s rule; 2) deduces the correspondent weights based on the computed BBA, and 3) uses an appropriate transfer function to activate the next layer neurons. In this paper, we demonstrate the effectiveness of our proposed method by using a real ED database. We prove that our proposed approach manages efficiently the uncertainty of the medical data sources and missing information, so improves the decision making and reduces errors and complexity.