GGATB-LSTM: Grouping and Global Attention-based Time-aware Bidirectional LSTM Medical Treatment Behavior Prediction

Abstract

In China, with the continuous development of national health insurance policies, more and more people have joined the health insurance. How to accurately predict patients future medical treatment behavior becomes a hotspot issue. The biggest challenge in this issue is how to improve the prediction performance by modeling health insurance data with high-dimensional time characteristics. At present, most of the research is to solve this issue by using Recurrent Neural Networks (RNNs) to construct an overall prediction model for the medical visit sequences. However, RNNs can not effectively solve the long-term dependence, and RNNs ignores the importance of time interval of the medical visit sequence. Additionally, the global model may lose some important content to different groups. In order to solve these problems, we propose a Grouping and Global Attention based Time-aware Bidirectional Long Short-Term Memory (GGATB-LSTM) model to achieve medical treatment behavior prediction. The model first constructs a heterogeneous information network based on health insurance data, and uses a tensor CANDECOMP/PARAFAC decomposition method to achieve similarity grouping. In terms of group prediction, a global attention and time factor are introduced to extend the bidirectional LSTM. Finally, the proposed model is evaluated by using real dataset, and conclude that GGATB-LSTM is better than other methods.