research-article

A Multi-attention Collaborative Deep Learning Approach for Blood Pressure Prediction

Authors:

Bei WangAuthors Info & Claims

ACM Transactions on Management Information Systems (TMIS), Volume 13, Issue 2

Article No.: 15, Pages 1 - 20

https://doi.org/10.1145/3471571

Published: 18 October 2021 Publication History

Abstract

We develop a deep learning model based on Long Short-term Memory (LSTM) to predict blood pressure based on a unique data set collected from physical examination centers capturing comprehensive multi-year physical examination and lab results. In the Multi-attention Collaborative Deep Learning model (MAC-LSTM) we developed for this type of data, we incorporate three types of attention to generate more explainable and accurate results. In addition, we leverage information from similar users to enhance the predictive power of the model due to the challenges with short examination history. Our model significantly reduces predictive errors compared to several state-of-the-art baseline models. Experimental results not only demonstrate our model’s superiority but also provide us with new insights about factors influencing blood pressure. Our data is collected in a natural setting instead of a setting designed specifically to study blood pressure, and the physical examination items used to predict blood pressure are common items included in regular physical examinations for all the users. Therefore, our blood pressure prediction results can be easily used in an alert system for patients and doctors to plan prevention or intervention. The same approach can be used to predict other health-related indexes such as BMI.

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Index Terms

A Multi-attention Collaborative Deep Learning Approach for Blood Pressure Prediction
1. Applied computing
  1. Life and medical sciences
    1. Health care information systems
2. Information systems
  1. Information systems applications
    1. Data mining
    2. Decision support systems
      1. Data analytics

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cover image ACM Transactions on Management Information Systems

ACM Transactions on Management Information Systems Volume 13, Issue 2

June 2022

261 pages

ISSN:2158-656X

EISSN:2158-6578

DOI:10.1145/3483345

Editor:
Daniel Zeng
Chinese Academy of Sciences, China

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Publication History

Published: 18 October 2021

Accepted: 01 June 2021

Revised: 01 May 2021

Received: 01 February 2021

Published in TMIS Volume 13, Issue 2

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National Key Research and Development Program of China
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