{
    "abstract": "The integration of data from diverse sources is not only crucial for addressing data scarcity in health informatics but also enables the use of complementary information from multiple datasets. However, the isolated nature of data collected from disparate sources (referred to as 'Silos') presents significant challenges in multi-source data integration due to inherent heterogeneity and differences in data structures, formats, and standards. Domain adaptation emerges as a key framework to transition from 'Silos' to 'Synthesis' by measuring and mitigating such discrepancies, enabling uniform representation and harmonization of multi-source data.\nThis study explores different approaches to healthcare data integration, highlighting the challenges associated with each type and discussing both general-purpose and healthcare-specific adaptation methods. We examine key research challenges and evaluate leading domain adaptation approaches, demonstrating their effectiveness and limitations in advancing healthcare data integration.\nThe findings highlight the potential of domain adaptation methods to significantly improve healthcare data integration while laying a foundation for future research.\nCurrent research often lacks a comprehensive analysis of how domain adaptation can effectively address the challenges associated with integrating multi-source and multi-modal healthcare datasets. This study serves as a valuable resource for healthcare professionals and researchers, providing guidance on leveraging domain adaptation techniques to mitigate domain discrepancies in healthcare data integration.",
    "authors": [
        {
            "affiliation": "Department of Computer Science, Wayne State University, 5057 Woodward Ave, Detroit, 48201, MI, USA. Electronic address: shelia.tuly@wayne.edu.",
            "firstname": "Shelia Rahman",
            "initials": "SR",
            "lastname": "Tuly"
        },
        {
            "affiliation": "Department of Computer Science, Wayne State University, 5057 Woodward Ave, Detroit, 48201, MI, USA. Electronic address: sima.ranjbari@wayne.edu.",
            "firstname": "Sima",
            "initials": "S",
            "lastname": "Ranjbari"
        },
        {
            "affiliation": "Department of Industrial and Systems Engineering, Wayne State University, 4th Street, Detroit, 48201, MI, USA. Electronic address: amurat@wayne.edu.",
            "firstname": "Ekrem Alper",
            "initials": "EA",
            "lastname": "Murat"
        },
        {
            "affiliation": "Department of Computer Science, Wayne State University, 5057 Woodward Ave, Detroit, 48201, MI, USA. Electronic address: suzan.arslanturk@wayne.edu.",
            "firstname": "Suzan",
            "initials": "S",
            "lastname": "Arslanturk"
        }
    ],
    "conclusions": "Current research often lacks a comprehensive analysis of how domain adaptation can effectively address the challenges associated with integrating multi-source and multi-modal healthcare datasets. This study serves as a valuable resource for healthcare professionals and researchers, providing guidance on leveraging domain adaptation techniques to mitigate domain discrepancies in healthcare data integration.",
    "copyrights": "Copyright \u00a9 2025 Elsevier Ltd. All rights reserved.",
    "doi": "10.1016/j.compbiomed.2025.110108",
    "journal": "Computers in biology and medicine",
    "keywords": [
        "Data integration",
        "Domain adaptation",
        "Healthcare",
        "Machine learning",
        "Multi-source data"
    ],
    "methods": null,
    "publication_date": "2025-04-11",
    "pubmed_id": "40209575",
    "results": "The findings highlight the potential of domain adaptation methods to significantly improve healthcare data integration while laying a foundation for future research.",
    "title": "From Silos to Synthesis: A comprehensive review of domain adaptation strategies for multi-source data integration in healthcare.",
    "xml": "<Element 'PubmedArticle' at 0x788502353a60>"
}