Hospitalization Behavior Prediction Based on Attention and Time Adjustment Factors in Bidirectional LSTM

Abstract

Predicting the future medical treatment behaviors of patients from historical health insurance data is an important research hotspot. The most important challenge of this issue is how to correctly model such temporal and high dimensional data to significantly improve the prediction performance. In this paper, we propose an Attention and Time adjustment factors based Bidirectional LSTM hospitalization behavior prediction model (ATB-LSTM). The model uses a hidden layer to preserve the impact state of medical visit sequences at different time on future prediction, and introduces the attention mechanism and the time adjustment factor to jointly determine the strength of the hidden state at different moments, which significantly improves the predictive performance of the model.