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Related Concept Videos

Determining Order of Reaction02:53

Determining Order of Reaction

Rate laws describe the relationship between the rate of a chemical reaction and the concentration of its reactants. In a rate law, the rate constant k and the reaction orders are determined experimentally by observing how the rate of reaction changes as the concentrations of the reactants are changed. A common experimental approach to the determination of rate laws is the method of initial rates. This method involves measuring reaction rates for multiple experimental trials carried out using...
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Heuristics01:21

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Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

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Related Experiment Video

Updated: Jun 14, 2026

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

A recommendation algorithm for automating corollary order generation.

Jeffrey Klann1, Gunther Schadow, J M McCoy

  • 1Regenstrief Institute, Indianapolis, IN, USA.

AMIA ... Annual Symposium Proceedings. AMIA Symposium
|March 31, 2010
PubMed
Summary
This summary is machine-generated.

Automated data-mining techniques can efficiently generate clinical decision support content. This study used an item-based collaborative filtering algorithm to suggest corollary orders, with physicians finding most clinically meaningful.

Related Experiment Videos

Last Updated: Jun 14, 2026

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

Area of Science:

  • Health Informatics
  • Clinical Decision Support Systems
  • Data Mining

Background:

  • Manual development of clinical decision support content is resource-intensive.
  • E-commerce recommendation algorithms offer potential for automating content generation.
  • Previous research indicates data-mining is suitable for suggesting corollary orders.

Purpose of the Study:

  • To explore the use of recommendation algorithms for automating the development of clinical decision support content.
  • To assess the feasibility of using data-mining techniques to generate corollary order suggestions.

Main Methods:

  • An item-based collaborative filtering algorithm was employed.
  • Association rule interestingness measures were used to augment the algorithm.
  • The algorithm processed 866,445 inpatient orders from 2007 to generate suggestions.

Main Results:

  • The algorithm generated 584 potential corollary orders.
  • A physician panel evaluated the top 92 suggestions.
  • 75.3% of suggestions were deemed clinically meaningful by the panel.

Conclusions:

  • Automated generation of corollary orders is feasible and confirms the utility of data-mining tools.
  • This approach can enhance the efficiency and quality of decision support content development.
  • Computerized augmentation can help capture local standards in clinical guidelines.