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A study of drug-reaction relationships in Australian drug safety data

- Mammadov, Musa, Saunders, Gary, Dekker, Evan

  • Authors: Mammadov, Musa , Saunders, Gary , Dekker, Evan
  • Date: 2003
  • Type: Text , Conference paper
  • Relation: Paper presented at the 2nd Australian Data Mining Workshop, Sydney, New South Wales : 8th December, 2003
  • Full Text: false
  • Reviewed:
  • Description: The sparse nature of voluntarily reported drug safety data benefits from a system that consolidates the massive amount of data into a manageable format for analysis. This has been done for Australian drug safety data by the Australian Adverse Drug Reaction Advisory Committee (ADRAC) for reactions using the systems organ class (SOC) ontology. There has long been a need for a similar kind of grouping to apply to drugs in this type of data. In ADRAC, drugs are currently listed by trade-name, where only some of these trade-names were assigned anatomical-therapeutic-chemical classification (ATC) codes. We assigned an ATC code for each ADRAC trade-name and show that this ontology facilitates the detection of drug class / reaction class associations at various levels of specificity. This allows different views of these associations (even very rare ones) and their significance measured for the development of more sensitive signal detection methods. We report that this ATC classification enables both the grouping of association rule approach that is useful for studying rare associations, and the development of an adverse reaction signal detection method.
  • Description: E1
  • Description: 2003000340

Empirical evaluation methods for multiobjective reinforcement learning algorithms

- Vamplew, Peter, Dazeley, Richard, Berry, Adam, Issabekov, Rustam, Dekker, Evan

  • Authors: Vamplew, Peter , Dazeley, Richard , Berry, Adam , Issabekov, Rustam , Dekker, Evan
  • Date: 2011
  • Type: Text , Journal article
  • Relation: Machine Learning Vol. 84, no. 1-2 (2011), p. 51-80
  • Full Text: false
  • Reviewed:
  • Description: While a number of algorithms for multiobjective reinforcement learning have been proposed, and a small number of applications developed, there has been very little rigorous empirical evaluation of the performance and limitations of these algorithms. This paper proposes standard methods for such empirical evaluation, to act as a foundation for future comparative studies. Two classes of multiobjective reinforcement learning algorithms are identified, and appropriate evaluation metrics and methodologies are proposed for each class. A suite of benchmark problems with known Pareto fronts is described, and future extensions and implementations of this benchmark suite are discussed. The utility of the proposed evaluation methods are demonstrated via an empirical comparison of two example learning algorithms. © 2010 The Author(s).

Attributes of expert anticipation should inform the design of virtual reality simulators to accelerate learning and transfer of skill

- Müller, Sean, Dekker, Evan, Morris-Binelli, Khaya, Piggott, Benjamin, Hoyne, Gerard, Christensen, Wayne, Fadde, Peter, Zaichkowsky, Leonard, Brenton, John, Hambrick, David

  • Authors: Müller, Sean , Dekker, Evan , Morris-Binelli, Khaya , Piggott, Benjamin , Hoyne, Gerard , Christensen, Wayne , Fadde, Peter , Zaichkowsky, Leonard , Brenton, John , Hambrick, David
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Sports Medicine Vol. 53, no. 2 (2023), p. 301-309
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  • Reviewed:
  • Description: Expert sport performers cope with a multitude of visual information to achieve precise skill goals under time stress and pressure. For example, a major league baseball or cricket batter must read opponent variations in actions and ball flight paths to strike the ball in less than a second. Crowded playing schedules and training load restrictions to minimise injury have limited opportunity for field-based practice in sports. As a result, many sports organisations are exploring the use of virtual reality (VR) simulators. Whilst VR synthetic experiences can allow greater control of visual stimuli, immersion to create presence in an environment, and interaction with stimuli, compared to traditional video simulation, the underpinning mechanisms of how experts use visual information for anticipation have not been properly incorporated into its content design. In themes, this opinion article briefly explains the mechanisms underpinning expert visual anticipation, as well as its learning and transfer, with a view that this knowledge can better inform VR simulator content design. In each theme, examples are discussed for improved content design of VR simulators taking into consideration its advantages and limitations relative to video simulation techniques. Whilst sport is used as the exemplar, the points discussed have implications for skill learning in other domains, such as military and law enforcement. It is hoped that our paper will stimulate improved content design of VR simulators for future research and skill enhancement across several domains. © 2022, The Author(s). Correction to: Sports Medicine https://doi.org/10.1007/s40279-022-01735-7, Page 1: The affiliation for Evan Dekker, which previously read: 2Academic Services and Support Directorate, University Drive, Mt. Helen, Ballarat, VIC 3350, Australia has now been updated to read: Academic Services and Support Directorate, Federation University, University Drive, Mt. Helen, Ballarat, VIC 3350, Australia. The original article has been corrected.
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