Predicting Hospital Attendance with Neural Networks and Bayesian Inference

Abstract: Missed hospital appointments is a globally acknowledged problem. In order to minimize the cost associated with this, attempts have been made to predict what appointments will be missed using statistical models and machine learning. However, in all previous models, information about a patient’s previous appointments has been ignored to some extent. In this thesis, a novel way of incorporating previous appointment data in more detail is proposed. This is done by firstly estimating a prior attendance probability based on general data using an artificial neural network, and then updating it using Bayesian inference. In the updating process, the information about a patient’s previous appointments is used in order to capture eventual unique patterns and behavior. This is done by weighting the outcome of the past appointments according to how similar those appointments are to the appointment that is to be predicted. Additionally, different ways of measuring the uncertainty of predictions are evaluated. The results show that weighting the outcome of previous appointments differently improves the performance of the predictions, which indicates that the proposed model manages to capture patients’ individual patterns. This improvement is apparent regardless of what model used to estimate the prior attendance probability. Furthermore, the uncertainty measurements correlated well with incorrect predictions, suggesting that they can be used to determine the reliability of a prediction.