Resource forecasting using Bayesian model reduction

摘要

A predictive model forecasts a medical resource need by making use of empirical Bayes estimation methods to determine a Dynamic Bayes Network (DBN) model. Exemplary resource needs that may be forecast include nurses, ventilators, hospital rooms, etc. The DBN model is estimated from retrieved data that is related to the resource to be forecast. The DBN model is simplified and a predictive model is generated based on the simplified model. The predictive model runs to forecast the predicted need for the resource. Embodiments are directed toward a predictive model development system that instructs an operator as to the structure of the data so that a model may be tailored based on the understanding of the operator. Embodiments are directed toward a model running system that indexes available models and also employs powerful statistical analysis techniques on behalf of a user to generate a predictive model with little or no user involvement in low-level modeling details.