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

Principles of data mining.

David J Hand1

  • 1Department of Mathematics, Imperial College London, London, UK. d.j.hand@imperial.ac.uk

Drug Safety
|July 3, 2007
PubMed
Summary
This summary is machine-generated.

This study explores data mining for discovering patterns in large datasets, focusing on tools for detecting adverse drug reactions in the pharmaceutical sector. It highlights methods for identifying both global and local structures relevant to drug safety.

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

Building back better needs better use of statistics.

Significance (Oxford, England)·2021
Same author

F*: an interpretable transformation of the F-measure.

Machine learning·2021
Same author

Validating and Verifying AI Systems.

Patterns (New York, N.Y.)·2020
Same author

Aspects of Data Ethics in a Changing World: Where Are We Now?

Big data·2018
Same author

From evidence to understanding: a commentary on Fisher (1922) 'On the mathematical foundations of theoretical statistics'.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2015
Same author

Never say never.

Scientific American·2014
Same journal

Availability and Communication of Risk Management Strategies for Pregnancy Category X Medicines across Australian Medicine Information Sources.

Drug safety·2026
Same journal

The Bidirectionality of Lawyer Reporting Bias in Disproportionality Analysis.

Drug safety·2026
Same journal

Safety of Biologic and Targeted Synthetic Disease-Modifying Antirheumatic Drugs in Rheumatoid Arthritis: A Longitudinal Analysis.

Drug safety·2026
Same journal

Developing a Hierarchical Algorithm to Identify Pregnancies and Determine Gestational Age from Nationwide Linked Health Data in Taiwan.

Drug safety·2026
Same journal

Safety and Effectiveness of Direct Oral Anticoagulants Versus Low-Molecular-Weight Heparin for Cancer-Associated Thrombosis: A Systematic Review and Meta-analysis.

Drug safety·2026
Same journal

Analytic Misjudgment of Drug Safety Evidence and Causality: From the Prosecutor's Fallacy and Simpson's Paradox to Artificial Intelligence.

Drug safety·2026
See all related articles

Area of Science:

  • Statistics
  • Data Mining
  • Pharmacovigilance

Background:

  • Data mining involves discovering valuable structures in large datasets, encompassing global (distribution shapes) and local (anomaly detection) aspects.
  • In pharmaceutical signal detection, the focus is primarily on identifying local anomalies, though understanding global background models is also crucial.

Purpose of the Study:

  • To provide an overview of data mining techniques and their statistical foundations.
  • To emphasize tools specifically applicable to the detection of adverse drug reactions.

Main Methods:

  • Overview of data mining principles.
  • Discussion of statistical methods for pattern recognition.
  • Application of data mining to signal detection in pharmaceuticals.

Related Experiment Videos

Main Results:

  • Data mining offers methods for analyzing large datasets to find significant patterns.
  • Specific techniques can be applied to identify potential adverse drug reactions from background data.

Conclusions:

  • Data mining, combined with statistical approaches, is valuable for adverse drug reaction detection.
  • Understanding both global and local data structures is essential for effective pharmaceutical signal detection.