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Clinical Research Informatics: Contributions from 2016.

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    Summary
    This summary is machine-generated.

    Clinical Research Informatics (CRI) research in 2016 highlighted machine learning for real-world data analysis. Key papers covered phenotype modeling, secure data sharing, data stewardship, and privacy risks in genomics.

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    Area of Science:

    • Informatics
    • Biomedical Research
    • Data Science

    Background:

    • Clinical Research Informatics (CRI) is crucial for advancing medical research.
    • Identifying significant contributions in CRI is essential for tracking progress.

    Purpose of the Study:

    • To summarize key contributions to Clinical Research Informatics (CRI) research.
    • To identify and select the best papers published in 2016 within CRI.

    Main Methods:

    • Bibliographic search using PubMed with MeSH and free terms for CRI.
    • Double-blind review process to select candidate papers.
    • Peer review by external reviewers and consensus meeting for final selection.

    Main Results:

    • Four best papers were selected from 452 published CRI papers in 2016.
    • Selected papers focused on machine learning for phenotype modeling, open-source tools for data integration (i2b2), FAIR data principles, and genomics data privacy.
    • A major trend identified was the use of "real-world data" (healthcare-generated, person health, patient-reported outcomes).

    Conclusions:

    • 2016 CRI research demonstrated a significant trend in utilizing "real-world data".
    • Advancements in machine learning offer new opportunities for data analysis.
    • Increased use of diverse data sources also presents new potential risks of privacy breaches.