Applying anatomical therapeutic chemical (ATC) and critical term ontologies to Australian drug safety data for association rules and adverse event signalling
- Authors: Saunders, Gary , Ivkovic, Sasha , Ghosh, Ranadhir , Yearwood, John
- Date: 2005
- Type: Text , Journal article
- Relation: Conferences in Research and Practice in Information Technology, Advances in Ontologies 2005: Proceedings of the Australasian Ontology Workshop AOW 2005 Vol. 58, no. (2005), p. 93-98
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- Description: C1
- Description: 2003001450
The automated identification and description of clusters for categorical data using conditional probabilities
- Authors: Ivkovic, Sasha , Yearwood, John , Ghosh, Ranadhir
- Date: 2004
- Type: Text , Conference paper
- Relation: Paper presented at CIMCA 2004: International Conference on Computational Intelligence for Modelling, Control & Automation, Gold Coast, Queensland : 12th July, 2004
- Full Text: false
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- Description: E1
- Description: 2003000885
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
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- 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
Visual grouping of association rules by clustering conditional probabilities for categorical data
- Authors: Ivkovic, Sasha , Ghosh, Ranadhir , Yearwood, John
- Date: 2005
- Type: Text , Book chapter
- Relation: Business Applications and Computational Intelligence p. 248-266
- Full Text: false
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- Description: We demonstrate the use of a visual data-mining tool for non-technical domain experts within organizations to facilitate the extraction of meaningful information and knowledge from in-house databases. The tool is mainly based on the basic notion of grouping association rules. Association rules are useful in discovering items that are frequently found together. However in many applications, rules with lower frequencies are often interesting for the user. Grouping of association rules is one way to overcome the rare item problem. However some groups of association rules are too large for ease of understanding. In this chapter we propose a method for clustering categorical data based on the conditional probabilities of association rules for data sets with large numbers of attributes. We argue that the proposed method provides non-technical users with a better understanding of discovered patterns in the data set.