标题:Hospitalization Behavior Prediction Based on Attention and Time Adjustment Factors in Bidirectional LSTM
作者:Cheng, Lin; Ren, Yongjian; Zhang, Kun; Pan, Li; Shi, Yuliang
通讯作者:Shi, Yuliang;Shi, YL;Shi, YL
作者机构:[Cheng, Lin; Ren, Yongjian; Zhang, Kun; Pan, Li; Shi, Yuliang] Shandong Univ, Sch Software, Jinan, Shandong, Peoples R China.; [Zhang, Kun; Shi, Yul 更多
会议名称:24th Int Conference on Database Systems for Advanced Applications / 6th Int Workshop on Big Data Management and Service / 4th Int Workshop on Big Data Quality Management / 3rd Int Workshop on Graph Data Management and Analysis
会议日期:APR 22-25, 2019
来源:DATABASE SYSTEMS FOR ADVANCED APPLICATIONS
出版年:2019
卷:11448
页码:397-401
DOI:10.1007/978-3-030-18590-9_53
关键词:Health insurance; Medical visit sequences; Attention mechanism; Time; adjustment factor; Hospitalization behavior prediction
摘要: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.
收录类别:CPCI-S;EI
资源类型:会议论文
TOP