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
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
Risk : Drug therapy versus adverse drug reaction. A case study approach with consumer, medical and expert views
- Authors: O'Brien, Michelle , Yearwood, John
- Date: 2004
- Type: Text , Conference paper
- Relation: Paper presented at the Health Informatics Conference 2004 - Let's make a difference with health ICT, Brisbane : 25th July, 2004
- Full Text: false
- Reviewed:
- Description: Extracts of three case studies, from a larger qualitative study, are used to illustrate an emerging theme about the issue of the risks versus the benefits of providing detailed information about adverse drug reactions (ADRs) to consumers. The case studies include the consumer, medical and expert views of a single event, a suspected adverse drug reaction. The consumers would like more information. The doctors expressed that providing information about all possible ADRs can result in some consumers choosing not to take medications due to the perceived risk of the medication. The ADR experts stated that providing ADR information to the consumers within the presented case studies, might have assisted in earlier detection of the ADR, resulting in a less severe reaction. This paper aims to illustrate this emerging theme of the risks versus the benefits of providing consumers with information about potential ADRs and suggest some ways of increasing the benefits and minimizing the risk. We then indicate how these finding may inform future development of ADR decision support systems.
- Description: E1
- Description: 2003000911
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
Using association and overlapping time window approach to detect drug reaction signals
- Authors: Ivkovic, Sasha , Saunders, Gary , Ghosh, Ranadhir , Yearwood, John
- Date: 2006
- Type: Text , Conference paper
- Relation: Paper presented at CIMCA 2005 International Conference on Computational Intelligence for Modelling Control & Automation jointly with IAWTIC 2005 International Conference on Intelligent Agents, Web Technologies & Internet Commerce, Vienna, Austria : 28th November, 2005 p. 1045-1053
- Full Text:
- Reviewed:
- Description: The problem with detecting adverse drug reactions (ADRs) from drugs is that they may not be obvious until long after they are widely prescribed. Part of the problem is these events are rare. This work describes an approach to signal detection of ADRs based on association rules (AR) in Australian drug safety data. This work was carried out using the Australian Adverse Drug Reactions Advisory Committee (ADRAC) database, which contains a hundred and thirty seven thousand records collected in 1972-2001 period. Many signal detection methods have been developed for drug safety data, most of which use a classical statistical approach. Some of these stratify the data using an ontology for reactions, but the application of drug ontologies to ADR signal detection methods has not been reported. We propose a novel approach for detecting various signal levels by using an overlapped windowing approach. The overlapping windows help to detect smooth transition of signal. We use association rules for measuring significant change over time for different hierarchical levels of drugs (using the Anatomical-Therapeutic-Chemical (ATC) system of drug classification ontology) and their reactions based on the System Organ Classes (SOC) ontology. Using association rules and their strength for different levels in the drug and reaction hierarchy, helps in the detection of signals at particular levels in higher order using a bottom up approach. The results of a preliminary investigation of ADRAC data using our method demonstrate that this approach could produce a powerful and robust ADR signal detection method.
- Description: E1
- Description: 2003001838
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