Damien McAullay

Mining risk patterns in medical data

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 and present an algorithm to make use of the property in risk pattern discovery. The method has been applied to a real world data set to find patterns associated with an allergic event for ACE inhibitors. The algorithm has generated some useful results for medical researchers.

Remote access methods for exploratory data analysis and statistical modelling: Privacy-Preserving Analytics ®

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 before release. In this paper we discuss the alternative approach of using a remote analysis server which does not enable any data release, but instead is designed to deliver useful results of user-specified statistical analyses with a low risk of disclosure. The techniques described in this paper enable a user to conduct a wide range of methods in exploratory data analysis, regression and survival analysis, while at the same time reducing the risk that the user can read or infer any individual record attribute value. We illustrate our methods with examples from biostatistics using publicly available data. We have implemented our techniques into a software demonstrator called Privacy-Preserving Analytics (PPA), via a web-based interface to the R software. We believe that PPA may provide an effective balance between the competing goals of providing useful information and reducing disclosure risk in some situations.

Signaling Potential Adverse Drug Reactions from Administrative Health Databases

The work is motivated by real-world applications of detecting Adverse Drug Reactions (ADRs) from administrative health databases. ADRs are a leading cause of hospitalization and death worldwide. Almost all current postmarket ADR signaling techniques are based on spontaneous ADR case reports, which suffer from serious underreporting and latency. However, administrative health data are widely and routinely collected. They, especially linked together, would contain evidence of all ADRs. To signal unexpected and infrequent patterns characteristic of ADRs, we propose a domain-driven knowledge representation Unexpected Temporal Association Rule (UTAR), its interestingness measure, unexlev, and a mining algorithm MUTARA (Mining UTARs given the Antecedent). We then establish an improved algorithm, HUNT, for highlighting infrequent and unexpected patterns by comparing their ranks based on unexlev with those based on traditional leverage. Various experimental results on real-world data substantiate that both MUTARA and HUNT can signal suspected ADRs while traditional association mining techniques cannot. HUNT can reliably shortlist statistically significantly more ADRs than MUTARA (p=0.00078). HUNT, e.g., not only shortlists the drug alendronate associated with esophagitis as MUTARA does, but also shortlists alendronate with diarrhoea and vomiting for older (age ¿ 60) females. We also discuss signaling ADRs systematically by using HUNT.

Mining unexpected associations for signalling potential adverse drug reactions from administrative health databases

Adverse reactions to drugs are a leading cause of hospitalisation 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 available. We introduce a new knowledge representation, Unexpected Temporal Association Rules (UTARs), to describe patterns characteristic of ADRs. Due to their unexpectedness and infrequency, existing techniques cannot perform effectively. To handle this unexpectedness we introduce a new interestingness measure, unexpected-leverage, and give a user-based exclusion technique for its calculation. Combining it with an event-oriented data preparation technique to handle infrequency, we develop a new algorithm, MUTARA, for mining simple UTARs. MUTARA effectively short-lists some known ADRs such as the disease esophagitis unexpectedly associated with the drug alendronate. Similarly, MUTARA signals atorvastatin followed by nizatidine or dicloxacillin which may be prescribed to treat its side effects stomach ulcer or urinary tract infection, respectively. Compared with association mining techniques, MUTARA signals potential ADRs more effectively.

Representing association classification rules mined from health data

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 two kinds of probability trees to present them. Both of them represent the risk of given adverse drug reaction for certain categories of patients in terms of risk ratios, which are familiar to medical practitioners. The first approach shows risk ratios when all rule conditions apply. The second presents the risk associated with a single risk factor with other parts of the rule identifying the cohort of the patient subpopulation. Thus, the probability trees can present clearly the risk of specific adverse drug reactions to prescribers.

Identifying risk groups associated with colorectal cancer

In this paper, we explore data mining techniques for the task of identifying and describing risk groups for colorectal cancer (CRC) from population based administrative health data. Association rule discovery, association classification and scalable clustering analysis are applied to the colorectal cancer patients’ profiles in contrast to background patients’ profiles. These data mining methods enable us to identify the most common characteristics of the colorectal cancer patients. The knowledge discovered by data mining methods which are quite different from traditional survey approaches. Although it is heuristic, the data mining methods may identify risk groups for further epidemiological study, such as older patients living near health facilities yet seldom utilising those facilities, and with respiratory and circulatory diseases.