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The arithmetic mean is usually skewed towards the larger values in the data set. Therefore, to avoid this inherent bias towards smaller values, the harmonic mean is used.
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Cohort Harmonization and Integrative Analysis From a Biomedical Engineering Perspective.

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    Data harmonization is crucial for integrating diverse health records, enabling precision medicine and advancing research in chronic diseases. This review outlines key steps and challenges for effective cohort harmonization.

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    Area of Science:

    • Biomedical Engineering
    • Health Informatics
    • Data Science

    Background:

    • Data sharing is essential for integrating heterogeneous health data sources.
    • Addressing unmet needs in chronic and other diseases requires integrating patient records and clinical information.
    • Cohort harmonization is vital for advancing patient-centered clinical research and precision medicine.

    Purpose of the Study:

    • To outline critical steps and milestones for data harmonization from a biomedical engineering viewpoint.
    • To explore the impact of cohort harmonization on patient stratification, biomarker identification, and precision medicine.
    • To discuss ethical, legal, and technological aspects of data harmonization.

    Main Methods:

    • Review of critical parts and milestones in data harmonization.
    • Presentation of matching techniques and ontologies for data schema creation.
    • Analysis of web technologies and data-collection tools for integration and interoperability.
    • Discussion of ethical and legal issues in individual-level data sharing.
    • Explicit analysis of use cases testing harmonization approaches.

    Main Results:

    • Cohort harmonization enables patient stratification, feature determination, and biomarker identification for disease phenotypes.
    • Web technologies and data-collection tools enhance data integration and interoperability.
    • Use cases demonstrate significant results in achieving research objectives through harmonization.
    • Ethical and legal considerations are crucial for evaluating harmonization potential.

    Conclusions:

    • Data harmonization is a key enabler for interdisciplinary research and precision medicine.
    • Establishing robust data schemas, matching techniques, and ontologies is critical.
    • Addressing ethical and legal issues is paramount for successful data sharing and harmonization.
    • Future trends point towards a roadmap for cohort harmonization in clinical medicine.