Relationships between different Australian interest rate swap markets
- Authors: Mammadov, Musa , Yearwood, John
- 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: 2003000893
Relationships between different term structures of Australian interest rate swap markets
- Authors: Mammadov, Musa , Yearwood, John
- Date: 2004
- Type: Text , Conference paper
- Relation: Paper presented at the First International Workshop on Intelligent Finance, IWIF 2004, Melbourne : 13th December, 2004
- Full Text: false
- Reviewed:
- Description: E1
- Description: 2003000894
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