Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Multi-database mining.

Mir S Siadaty1, James H Harrison

  • 1Division of Clinical Informatics, Department of Public Health Sciences, University of Virginia, Suite 3181 West Complex, 1335 Hospital Drive Charlottesville, VA 22908, USA. mirsiadaty@virginia.edu

Clinics in Laboratory Medicine
|January 16, 2008
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Future of Artificial Intelligence-Machine Learning Trends in Pathology and Medicine.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc·2025
Same author

Introduction to Artificial Intelligence and Machine Learning for Pathology.

Archives of pathology & laboratory medicine·2021
Same author

Streamlining Quality Review of Mass Spectrometry Data in the Clinical Laboratory by Use of Machine Learning.

Archives of pathology & laboratory medicine·2019
Same author

The College of American Pathologists Biorepository Accreditation Program: Results from the First 5 Years.

Biopreservation and biobanking·2018
Same author

Pathology Informatics Essentials for Residents: A Flexible Informatics Curriculum Linked to Accreditation Council for Graduate Medical Education Milestones (a secondary publication).

Academic pathology·2017
Same author

Early Palliative Care Reduces End-of-Life Intensive Care Unit (ICU) Use but Not ICU Course in Patients with Advanced Cancer.

The oncologist·2017
Same journal

Advances in Hemostasis Laboratory Testing.

Clinics in laboratory medicine·2026
Same journal

Extracellular Vesicles in Hemostasis.

Clinics in laboratory medicine·2026
Same journal

Thrombin Generation Assay: Ready for Prime Time.

Clinics in laboratory medicine·2026
Same journal

Viscoelastic Testing for the Laboratorian: Recent Advances and Practical Advice.

Clinics in laboratory medicine·2026
Same journal

Practical Recommendations for Harmonization of Hemostasis Testing Across Hospital Sites.

Clinics in laboratory medicine·2026
Same journal

The Role of Hypoxia in Vascular Endothelial Dysfunction and Venous Thromboembolism.

Clinics in laboratory medicine·2026
See all related articles

Discovering biomedical insights requires analyzing distributed data. Dual mining enhances data mining by analyzing databases separately yet concurrently, preserving unique information lost during integration.

Area of Science:

  • Biomedical informatics
  • Data mining
  • Database management

Background:

  • Biomedical data is frequently fragmented across multiple databases.
  • Data integration for mining can lead to loss of unique analytical information.
  • Existing methods for mining distributed data may not fully leverage individual database characteristics.

Purpose of the Study:

  • To introduce and demonstrate a novel approach called "dual mining" for analyzing distributed biomedical data.
  • To highlight the advantages of analyzing databases separately but concurrently over traditional integration methods.
  • To improve the identification of relevant association patterns in biomedical target databases.

Main Methods:

  • Concurrent analysis of a target database with a related knowledge base.

Related Experiment Videos

  • Utilizing "dual mining" to process multiple databases simultaneously without full integration.
  • Focusing on preserving unique data representations and analytical perspectives from each source.

    • Main Results:

    • Dual mining effectively identifies association patterns within a target database.
    • The approach preserves valuable information that would be lost through data aggregation.
    • Concurrent analysis with a knowledge base refines the selection of patterns for further investigation.

    Conclusions:

    • Dual mining offers a powerful strategy for extracting more meaningful insights from distributed biomedical datasets.
    • This method enhances the efficiency and relevance of data mining in complex biomedical research.
    • Preserving the integrity of individual databases is crucial for maximizing analytical utility.