Medical Treatment Migration Prediction in Healthcare via Attention-Based Bidirectional GRU

With the rapid expansion of the number of floating populations in China, a large number of people are gradually migrating to different hospitals to seek medical treatment. How to accurately predict the future medical treatment behaviors of patients has become an important research issue in healthcare. In this paper, an Attention-based Bidirectional Gated Recurrent Unit (AB-GRU) medical treatment migration prediction model is proposed to predict which hospital patients will go to in the future. The model groups patients who are prone to medical treatment migration, and achieves disease prediction and medical treatment migration prediction for each group. In terms of disease prediction, considering the predictive performance problem of a single prediction algorithm, a standard deviation weight recombination method is used to achieve disease prediction. When disease prediction has been completed, considering the impact of medical visit on the future medical behavior, on the basis of bidirectional gated recurrent unit (GRU) framework, we introduce an attention mechanism to determine the strength of hidden state at different moments, which can improve the predictive performance of the model. The experiment demonstrates that the predictive model proposed in this paper is more accurate than the traditional predictive models.