Dynamic causal mining

Causality plays a central role in human reasoning, in particular, in common human decision-making, by providing a basis for strategy selection. The main aim of the research reported in this thesis is to develop a new way to identify dynamic causal relationships between attributes of a system. The first part of the thesis introduces the development of a new data mining algorithm, called Dynamic Causal Mining (DCM), which extracts rules from data sets based on simultaneous time stamps. The rules derived can be combined into policies, which can simulate the future behaviour of systems. New rules can be added to the policies depending on the degree of accuracy. In addition, facilities to process categorical or numerical attributes directly and approaches to prune the rule set efficiently are implemented in the DCM algorithm. The second part of the thesis discusses how to improve the DCM algorithm in order to identify delay and feedback relationships. Fuzzy logic is applied to manage the rules and policies flexibly and accurately during the learning process and help the algorithm to find feasible solutions. The third part of the thesis describes the application of the suggested algorithm to a problem in the game-theoretic domain. This part concludes with the suggestion to use concept lattices as a method to represent and structure the discovered knowledge

Pharmacovigilance Decision Support : The value of Disproportionality Analysis Signal Detection Methods, the development and testing of Covariability Techniques, and the importance of Ontology

The cost of adverse drug reactions to society in the form of deaths, chronic illness, foetal malformation, and many other effects is quite significant. For example, in the United States of America, adverse reactions to prescribed drugs is around the fourth leading cause of death. The reporting of adverse drug reactions is spontaneous and voluntary in Australia. Many methods that have been used for the analysis of adverse drug reaction data, mostly using a statistical approach as a basis for clinical analysis in drug safety surveillance decision support. This thesis examines new approaches that may be used in the analysis of drug safety data. These methods differ significantly from the statistical methods in that they utilize co variability methods of association to define drug-reaction relationships. Co variability algorithms were developed in collaboration with Musa Mammadov to discover drugs associated with adverse reactions and possible drug-drug interactions. This method uses the system organ class (SOC) classification in the Australian Adverse Drug Reaction Advisory Committee (ADRAC) data to stratify reactions. The text categorization algorithm BoosTexter was found to work with the same drug safety data and its performance and modus operandi was compared to our algorithms. These alternative methods were compared to a standard disproportionality analysis methods for signal detection in drug safety data including the Bayesean mulit-item gamma Poisson shrinker (MGPS), which was found to have a problem with similar reaction terms in a report and innocent by-stander drugs. A classification of drug terms was made using the anatomical-therapeutic-chemical classification (ATC) codes. This reduced the number of drug variables from 5081 drug terms to 14 main drug classes. The ATC classification is structured into a hierarchy of five levels. Exploitation of the ATC hierarchy allows the drug safety data to be stratified in such a way as to make them accessible to powerful existing tools. A data mining method that uses association rules, which groups them on the basis of content, was used as a basis for applying the ATC and SOC ontologies to ADRAC data. This allows different views of these associations (even very rare ones). A signal detection method was developed using these association rules, which also incorporates critical reaction terms.Doctor of Philosoph

LIPIcs, Volume 251, ITCS 2023, Complete Volume

LIPIcs, Volume 251, ITCS 2023, Complete Volum