Best practices for multimodal clinical data management and integration: An atopic dermatitis research case
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- Ohta Tazro
- Medical Data Mathematical Reasoning Team, Advanced Data Science Project, RIKEN Information R&D and Strategy Headquarters, RIKEN Institute for Advanced Academic Research, Chiba University Department of Artificial Intelligence Medicine, Graduate School of Medicine, Chiba University
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- Hananoe Ayaka
- Medical Data Mathematical Reasoning Team, Advanced Data Science Project, RIKEN Information R&D and Strategy Headquarters, RIKEN Laboratory for Developmental Genetics, RIKEN Center for Integrative Medical Sciences, RIKEN Department of Dermatology, Keio University School of Medicine
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- Fukushima-Nomura Ayano
- Department of Dermatology, Keio University School of Medicine
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- Ashizaki Koichi
- Laboratory for Developmental Genetics, RIKEN Center for Integrative Medical Sciences, RIKEN Department of Dermatology, Keio University School of Medicine Advanced Data Science Project, RIKEN Information R&D and Strategy Headquarters, RIKEN
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- Sekita Aiko
- Laboratory for Developmental Genetics, RIKEN Center for Integrative Medical Sciences, RIKEN
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- Seita Jun
- Laboratory for Integrative Genomics, RIKEN Center for Integrative Medical Sciences, RIKEN Medical Data Deep Learning Team, Advanced Data Science Project, RIKEN Information R&D and Strategy Headquarters, RIKEN Medical Data Sharing Unit, Infrastructure Research and Development Division, RIKEN Information R&D and Strategy Headquarters, RIKEN
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- Kawakami Eiryo
- Medical Data Mathematical Reasoning Team, Advanced Data Science Project, RIKEN Information R&D and Strategy Headquarters, RIKEN Institute for Advanced Academic Research, Chiba University Department of Artificial Intelligence Medicine, Graduate School of Medicine, Chiba University
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- Sakurada Kazuhiro
- Advanced Data Science Project, RIKEN Information R&D and Strategy Headquarters, RIKEN Department of Extended Intelligence for Medicine, The Ishii-Ishibashi Laboratory, Keio University School of Medicine
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- Amagai Masayuki
- Department of Dermatology, Keio University School of Medicine Laboratory for Skin Homeostasis, RIKEN Center for Integrative Medical Sciences, RIKEN
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- Koseki Haruhiko
- Laboratory for Developmental Genetics, RIKEN Center for Integrative Medical Sciences, RIKEN
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- Kawasaki Hiroshi
- Laboratory for Developmental Genetics, RIKEN Center for Integrative Medical Sciences, RIKEN Department of Dermatology, Keio University School of Medicine Laboratory for Skin Homeostasis, RIKEN Center for Integrative Medical Sciences, RIKEN
抄録
<p>Background: In clinical research on multifactorial diseases such as atopic dermatitis, data-driven medical research has become more widely used as means to clarify diverse pathological conditions and to realize precision medicine. However, modern clinical data, characterized as large-scale, multimodal, and multi-center, causes difficulties in data integration and management, which limits productivity in clinical data science.</p><p>Methods: We designed a generic data management flow to collect, cleanse, and integrate data to handle different types of data generated at multiple institutions by 10 types of clinical studies. We developed MeDIA (Medical Data Integration Assistant), a software to browse the data in an integrated manner and extract subsets for analysis.</p><p>Results: MeDIA integrates and visualizes data and information on research participants obtained from multiple studies. It then provides a sophisticated interface that supports data management and helps data scientists retrieve the data sets they need. Furthermore, the system promotes the use of unified terms such as identifiers or sampling dates to reduce the cost of pre-processing by data analysts. We also propose best practices in clinical data management flow, which we learned from the development and implementation of MeDIA.</p><p>Conclusions: The MeDIA system solves the problem of multimodal clinical data integration, from complex text data such as medical records to big data such as omics data from a large number of patients. The system and the proposed best practices can be applied not only to allergic diseases but also to other diseases to promote data-driven medical research.</p>
収録刊行物
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- Allergology International
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Allergology International 73 (2), 255-263, 2024
一般社団法人日本アレルギー学会
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キーワード
詳細情報 詳細情報について
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- CRID
- 1390862776829837312
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- ISSN
- 14401592
- 13238930
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- Crossref
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- 抄録ライセンスフラグ
- 使用不可