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 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
Multi label classification and drug-reaction associations using global optimization techniques
- Authors: Mammadov, Musa , Yearwood, John , Aliyea, Leyla
- Date: 2004
- Type: Text , Conference paper
- Relation: Paper presented at ICOTA6: 6th International Conference on Optimization - Techniques and Applications, Ballarat, Victoria : 9th December, 2004
- Full Text: false
- Reviewed:
- Description: E1
- Description: 2003000890
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