标题:Medical treatment migration behavior prediction and recommendation based on health insurance data
作者:Cheng, Lin; Shi, Yuliang; Zhang, Kun
作者机构:[Cheng, Lin; Shi, Yuliang; Zhang, Kun] Shandong Univ, Sch Software, Jinan, Peoples R China.; [Shi, Yuliang; Zhang, Kun] Dareway Software Co Ltd, Jin 更多
通讯作者:Shi, Yuliang
通讯作者地址:Shi, YL (corresponding author), Shandong Univ, Sch Software, Jinan, Peoples R China.; Shi, YL (corresponding author), Dareway Software Co Ltd, Jinan, 更多
来源:WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
出版年:2020
卷:23
期:3
页码:2023-2042
DOI:10.1007/s11280-020-00781-3
关键词:health insurance data; medical treatment migration prediction; medical; treatment recommendation
摘要:How to accurately predict the future medical treatment behaviors of patients from the historical health insurance data 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 considers the impact of medical visit on the future medical behavior, on the basis of Bidirectional Gated Recurrent Unit (B-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. Due to medical treatment in different places has an important impact on the distribution of health insurance funds, the individual patient would be expected to the appropriate hospital and get the appropriate medical treatment. Therefore, when medical treatment prediction has been completed, this paper proposes a Similarity and Double-layer CNN-based (SD_CNN) medical treatment migration recommendation model. The model introduces a CNN framework to achieve patient similarity learning, and compares similarities to recommend whether patients need medical treatment migration. Finally, the experiment demonstrates that the model proposed in this paper is more accurate than other models.
收录类别:EI;SCOPUS;SCIE
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081925147&doi=10.1007%2fs11280-020-00781-3&partnerID=40&md5=3ccece38483d167cbfd478234683c30a
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