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

Published: 03 May 2021 Publication History

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.

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Published In

cover image ACM Transactions on Knowledge Discovery from Data
ACM Transactions on Knowledge Discovery from Data  Volume 15, Issue 3
June 2021
533 pages
ISSN:1556-4681
EISSN:1556-472X
DOI:10.1145/3454120
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

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Publication History

Published: 03 May 2021
Accepted: 01 December 2020
Revised: 01 October 2020
Received: 01 May 2019
Published in TKDD Volume 15, Issue 3

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Author Tags

  1. Health insurance
  2. medical visit sequence
  3. similarity grouping
  4. medical treatment behavior prediction

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  • Research-article
  • Refereed

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  • National Key Research and Development Plan of China

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  • (2024)HgbNet: Predicting Hemoglobin Level/Anemia Degree From Irregular EHRIEEE Access10.1109/ACCESS.2024.347369312(144837-144854)Online publication date: 2024
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