Using anatomical therapeutic chemical (ATC) classification to reduce combinatorial complexity for Australian drug safety data analysis
- Authors: Saunders, Gary , Mammadov, Musa , Ivkovic, Sasha
- Date: 2005
- Type: Text , Conference paper
- Relation: Paper presented at the Sixteenth Australasian Workshop on Combinatorial Algorithms, Ballarat, Victoria : 18th - 21st September, 2005
- Full Text: false
- Reviewed:
- Description: E1
- Description: 2003001449
A comparison of two methods to establish drug-reaction relationships in the ADRAC database
- Authors: Mammadov, Musa , Saunders, Gary
- Date: 2004
- Type: Text , Conference paper
- Relation: Paper presented at the Fourth International ICSC Symposium on Engineering of Intelligent Systems (EIS 2004), Island of Madeira, Portugal, Island of Madeira, Portugal : 29th February, 2004
- Full Text: false
- Reviewed:
- Description: Adverse drug reactions (ADRs) are estimated to be one of the leading causes of death. Many national and international agencies have set up databases of ADR reports for the express purpose of determining the relationship between drugs and adverse reactions that they cause. We formulate the drug-reaction relationship problem as a continuous optimization problem and utilize C-GRASP, a new continuous global optimization heuristic, to approximately determine the relationship between drugs and adverse reactions. Our approach is compared against others in the literature and is shown to find better solutions. 1.
- Description: E1
- Description: 2003000897
A fuzzy derivative approach to classification of outcomes from the ADRAC database
- Authors: Mammadov, Musa , Saunders, Gary , Yearwood, John
- Date: 2004
- Type: Text , Journal article
- Relation: International Transactions in Operational Research Vol. 11, no. 2 (2004), p. 169-180
- Full Text: false
- Reviewed:
- Description: The Australian Adverse Drug Reaction Advisory Committee (ADRAC) database has been collected and maintained by the Therapeutic Goods Administration. In this paper we study a part of his database (Card2) which contains records having just reactions from the Cardiovascular group. Drug-reaction relationships are presented by a vector of degrees which shows the degree of association of a drug with each class of reactions. In this work we examine these relationships in the classification of reaction outcomes. A modified version of the fuzzy derivative method (FDM2) is used for classification.
- Description: C1
- Description: 2003000895
A study of drug-reaction relationships in Australian drug safety data
- 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
Analysis of cardiovascular adverse drug reactions from the ADRAC database
- Authors: Mammadov, Musa , Saunders, Gary
- Date: 2003
- Type: Text , Conference paper
- Relation: Paper presented at the APAC Conference and Exhibition on Advanced Computing, Grid Applications and eResearch, Gold Coast, Queensland : 29th September, 2003
- Full Text: false
- Reviewed:
- Description: E1
- Description: 2003000342
Fuzzy set analysis of Australian drug safety data
- Authors: Mammadov, Musa , Saunders, Gary
- Date: 2002
- Type: Text , Conference paper
- Relation: Paper presented at HIC 2002, Melbourne : 5th August, 2002
- Full Text: false
- Reviewed:
- Description: This report describes a preliminary analysis of the Australian Adverse Drug Reaction Database (ADRAC) using a new approach, which is being developed for the analysis of this drug safety data. This differs significantly from the statistical methods that have been used in that we utilize vectors of degrees to define drug-reaction relationships. A quasi-classification algorithm was developed to discover drugs associated with adverse reactions and possible drug-drug interactions. The machine learning algorithm FDM (Fuzzy Derivative Method) was used to predict good and bad outcomes based on observed reactions and potential vectors of degrees. This work extends existing methods for drug safety analysis and should also be of general interest in the field of data mining.
- Description: E1
- Description: 2003000055