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Adverse reactions to drugs are a leading cause of hospitalisa-tion and death worldwide. Most post-marketing Adverse Drug Reaction (ADR) detection techniques analyse spontaneous ADR reports which underestimate ADRs significantly. This paper aims to signal ADRs from administrative health databases in which data are collected routinely and are readily(More)
In this paper, we discuss a problem of finding risk patterns in medical data. We define risk patterns by a statistical metric, relative risk, which has been widely used in epidemiological research. We characterise the problem of mining risk patterns as an optimal rule discovery problem. We study an anti-monotone property for mining optimal risk pattern sets(More)
In various real-world applications, it is very useful mining unanticipated episodes where certain event patterns unexpectedly lead to outcomes, e.g., taking two medicines together sometimes causing an adverse reaction. These unanticipated episodes are usually unexpected and infrequent, which makes existing data mining techniques, mainly designed to find(More)
There are many methods for finding association rules in very large data. However it is well known that most general association rule discovery methods find too many rules, which include a lot of uninteresting rules. Furthermore, the performances of many such algorithms deteriorate when the minimum support is low. They fail to find many interesting rules(More)
An association classification algorithm has been developed to explore adverse drug reactions in a large medical transaction dataset with unbalanced classes. Rules discovered can be used to alert medical practitioners when prescribing drugs, to certain categories of patients, to potential adverse effects. We assess the rules using survival charts and propose(More)
BACKGROUND In Australia there is a socioeconomic gradient in morbidity and mortality favouring socioeconomically advantaged people, much of which is accounted for by ischaemic heart disease. This study examines if Australia's universal health care system, with its mixed public/private funding and delivery model, may actually perpetuate this inequity. We do(More)
Australia has extensive administrative health data collected by Commonwealth and state agencies. The value of the health data is how quickly and effectively the data can be converted into useful knowledge independent of its quantity and quality. Using a unique cleaned and linked administrative health dataset we address the problem of empirically defining(More)