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2023 Fiscal Year Annual Research Report

Secure, Precise and Fast Sequential Pattern Mining with Learning Data Distribution

Research Project

Project/Area Number 21K17746
Research InstitutionOchanomizu University

Principal Investigator

Le Hieu・Hanh  お茶の水女子大学, 文理融合 AI・データサイエンスセンター, 准教授 (60813996)

Project Period (FY) 2021-04-01 – 2024-03-31
Keywordsdata mining / privacy / medical data
Outline of Annual Research Achievements

This study aims to present a method for eliminating the need for trust in sequential pattern mining while preserving privacy and providing secure, precise, and fast sequential data analysis that carefully learns the data distribution.
Secure sequential data analysis of sequential medical data has been extensively studied. In detail, several methods to analyze the sequence variants (SV) from more than one hospital have been designed, such as comparing SVs, identifying factors that led to branches in SVs, etc. The proposed methods were rigorously evaluated using real medical data from more than 20 hospitals focusing on multiple diseases. As shown to be highly effective, the methods can be expected to be applied to other sequential data types.
Then, an appropriate amount of noise is added to the original frequency to ensure privacy when estimating the frequency of the sequences. Based on data distribution, only related data is added to the analysis to speed up the analysis while preserving high utility.
Finally, the sensitive medical data was analyzed securely with careful access control management to enhance privacy.

  • Research Products

    (3 results)

All 2023

All Presentation (3 results) (of which Int'l Joint Research: 1 results)

  • [Presentation] 千年カルテの匿名加工医療情報を利用した多医療機関の電子カルテに対するシーケンス解析2023

    • Author(s)
      Le Hieu Hanh, 松尾亮輔, 山﨑 友義, 横田 治夫
    • Organizer
      第43回医療情報学連合大会(第24回日本医療情報学会学術大会)
  • [Presentation] COVID-19の電子カルテ履歴からの医療指示シーケンスパターン変化時期の抽出2023

    • Author(s)
      趙 子泰, Le Hieu Hanh, 山﨑 友義, 荒木 賢二, 横田 治夫
    • Organizer
      第27回日本医療情報学会春季学術大会
  • [Presentation] Analysis of Transitions in Differences between Frequent Medical-order Sequences for COVID-192023

    • Author(s)
      Zitai Zhao, Yuki Yasumitsu, Hieu Hanh Le, Tomoyoshi Yamazaki, Kenji Araki, Haruo Yokota
    • Organizer
      The 36th IEEE International Symposium on Computer-Based Medical Systems
    • Int'l Joint Research

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Published: 2024-12-25  

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