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Related Concept Videos

Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
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Analyte Adsorption and Distribution01:09

Analyte Adsorption and Distribution

In certain chromatographic separations, solutes transfer between the mobile phase and the stationary phase via sorption, which typically refers to the process of adsorption. For many chromatographic systems, the sorption process often depends on the polarity of the compounds—an expression of the overall dipole moment within the molecule. During the separation process, there is competition between the solute and solvent for adsorption to the stationary phase. Highly polar compounds and solvents...
How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
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Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a problem,...

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Related Experiment Video

Updated: May 9, 2026

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
09:20

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

Towards on-line ADR detection: an innovative data cube-based approach.

Wen-Yang Lin1, Jhih-Wei Du

  • 1Department of Computer Science and Information Engineering, National University of Kaohsiung, Taiwan.

Studies in Health Technology and Informatics
|August 8, 2013
PubMed
Summary
This summary is machine-generated.

We developed a novel Multivalued-Dimension Contingency Cube (MDC-cube) for faster Adverse Drug Reaction (ADR) detection. This method significantly improves the speed of identifying ADRs, particularly those from drug interactions, compared to existing techniques.

Related Experiment Videos

Last Updated: May 9, 2026

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
09:20

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

Area of Science:

  • Pharmacovigilance
  • Data Mining
  • Computational Pharmacology

Background:

  • Adverse Drug Reactions (ADRs) detection is critical in pharmacovigilance.
  • Existing methods often prioritize accuracy over detection speed.
  • Efficient ADR detection, especially for drug interactions, remains a challenge.

Purpose of the Study:

  • To introduce a novel computational approach for fast and efficient ADR detection.
  • To address the performance limitations of current ADR detection methodologies.
  • To facilitate multidimensional, fast, and online detection of ADRs, including those from drug interactions.

Main Methods:

  • Proposed the Multivalued-Dimension Contingency Cube (MDC-cube) concept.
  • Developed a method leveraging MDC-cube for multidimensional data analysis.
  • Applied the method to the FDA AERS dataset for empirical evaluation.

Main Results:

  • The MDC-cube based method demonstrated significantly improved performance.
  • Achieved an order of magnitude speed increase compared to the ABCM-MS method.
  • Successfully facilitated fast and online detection of ADRs, including drug interactions.

Conclusions:

  • The MDC-cube approach offers a substantial performance enhancement for ADR detection.
  • This method is particularly effective for identifying ADRs stemming from drug interactions.
  • The findings suggest a promising direction for real-time pharmacovigilance systems.