Ross Sparks

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This paper is concerned with the challenge of enabling the use of confidential or private data for research and policy analysis, while protecting confidentiality and privacy by reducing the risk of disclosure of sensitive information. Traditional solutions to the problem of reducing disclosure risk include releasing de-identified data and modifying data(More)
Sharing health data for research purposes across data custodian boundaries poses technical, organisational and ethical challenges. In beginning to address some of the technical challenges, we describe a service oriented architecture for a proposed Health Research Data Network (HRDN). The HRDN architecture supports services to manage data access and use by(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)
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)
In this expository paper we give an overview of some statistical methods for the monitoring of social networks. We discuss the advantages and limitations of various methods as well as some relevant issues. One of our primary contributions is to give the relationships between network monitoring methods and monitoring methods in engineering statistics and(More)
Automated public health records provide the necessary data for rapid outbreak detection. An adaptive exponentially weighted moving average EWMA plan is developed for signalling unusually high incidence when monitoring a time series of nonhomogeneous daily disease counts. A Poisson transitional regression model is used to fit background/expected trend in(More)