research-article

Methods for Analyzing Medical-Order Sequence Variants in Sequential Pattern Mining for Electronic Medical Record Systems

Authors:

Tatsuhiro Yamada,

Takatoshi Sakamoto,

Ryosuke Matsuo,

Tomoyoshi Yamazaki,

Haruo YokotaAuthors Info & Claims

ACM Transactions on Computing for Healthcare, Volume 4, Issue 1

Article No.: 3, Pages 1 - 28

https://doi.org/10.1145/3561825

Published: 30 March 2023 Publication History

Abstract

Electronic medical record systems have been adopted by many large hospitals worldwide, enabling the recorded data to be analyzed by various computer-based techniques to gain a better understanding of hospital-based disease treatments. Among such techniques, sequential pattern mining, already widely used for data mining and knowledge discovery in other application domains, has shown great potential for discovering frequent patterns in sequences of disease treatments. However, studies have yet to evaluate the use of medical-order sequence variants, where a “frequent pattern” can include some limited variations to the pattern, or have considered the factors that lead to these variants. Such a study would be meaningful for medical tasks such as improving the quality of a particular treatment method, comparing treatments with multiple hospitals, recommending the best-suited treatment for each patient, and optimizing the running costs in hospitals. This article proposes methods for evaluating medical-order sequence variants and understanding variant factors based on a statistical approach. We consider the safety and efficiency of sequences and related information about the variants, such as gender, age, and test results from hospitals. Our proposal has been demonstrated as effective by experimentally evaluating an electronic medical record system’s real dataset and obtaining feedback from medical workers. The experimental results indicate that the medical treatment history and specimen test results after hospitalization are significant in identifying the factors that lead to variants.

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Cited By

Le HYasumitsu YMatsuo RYamazaki TYokota H(2024)A Clustering-based Sequence Variants Analysis Method for Electronic Medical Records of Multimedical Institutions2024 IEEE 7th International Conference on Multimedia Information Processing and Retrieval (MIPR)10.1109/MIPR62202.2024.00113(653-659)Online publication date: 7-Aug-2024
https://doi.org/10.1109/MIPR62202.2024.00113
Sala PZare O(2024)Sequence-Walking Decision Tree for Multivariate Healthcare Data2024 IEEE 12th International Conference on Healthcare Informatics (ICHI)10.1109/ICHI61247.2024.00011(21-30)Online publication date: 3-Jun-2024
https://doi.org/10.1109/ICHI61247.2024.00011
Zhao ZYasumitsu YLe HYamazaki TAraki KYokota H(2023)Analysis of Transitions in Differences between Frequent Medical-order Sequences for COVID-192023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS)10.1109/CBMS58004.2023.00297(666-671)Online publication date: Jun-2023
https://doi.org/10.1109/CBMS58004.2023.00297

Index Terms

Methods for Analyzing Medical-Order Sequence Variants in Sequential Pattern Mining for Electronic Medical Record Systems
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 Computing for Healthcare

ACM Transactions on Computing for Healthcare Volume 4, Issue 1

January 2023

217 pages

EISSN:2637-8051

DOI:10.1145/3582897

Editors:
Insup Lee
University of Pennsylvania, USA
,
John A. Stankovic
University of Virginia, USA
,
Gang Zhou
College of William and Mary, USA

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Publication History

Published: 30 March 2023

Online AM: 12 September 2022

Accepted: 24 August 2022

Revised: 16 June 2022

Received: 19 October 2021

Published in HEALTH Volume 4, Issue 1

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Health Labour Sciences Research Grant (Ministry of Health, Labour and Welfare, Japan)
Grants-in-Aid for Scientific Research
Grants-in-Aid for Early-Career Scientists
Japan Society for the Promotion of Science

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Cited By

Le HYasumitsu YMatsuo RYamazaki TYokota H(2024)A Clustering-based Sequence Variants Analysis Method for Electronic Medical Records of Multimedical Institutions2024 IEEE 7th International Conference on Multimedia Information Processing and Retrieval (MIPR)10.1109/MIPR62202.2024.00113(653-659)Online publication date: 7-Aug-2024
https://doi.org/10.1109/MIPR62202.2024.00113
Sala PZare O(2024)Sequence-Walking Decision Tree for Multivariate Healthcare Data2024 IEEE 12th International Conference on Healthcare Informatics (ICHI)10.1109/ICHI61247.2024.00011(21-30)Online publication date: 3-Jun-2024
https://doi.org/10.1109/ICHI61247.2024.00011
Zhao ZYasumitsu YLe HYamazaki TAraki KYokota H(2023)Analysis of Transitions in Differences between Frequent Medical-order Sequences for COVID-192023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS)10.1109/CBMS58004.2023.00297(666-671)Online publication date: Jun-2023
https://doi.org/10.1109/CBMS58004.2023.00297

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