title: Feature selection for examining behavior by pathology laboratories.
abstract: Australia has a universal health insurance scheme called Medicare, which is managed by Australia's Health Insurance Commission. Medicare payments for pathology services generate voluminous transaction data on patients, doctors and pathology laboratories. The Health Insurance Commission (HIC) currently uses predictive models to monitor compliance with regulatory requirements. The HIC commissioned a project to investigate the generation of new features from the data. Feature generation has not appeared as an important step in the knowledge discovery in databases (KDD) literature. New interesting features for use in predictive modeling are generated. These features were summarized, visualized and used as inputs for clustering and outlier detection methods. Data organization and data transformation methods are described for the efficient access and manipulation of these new features.
authors.affiliation: CSIRO Mathematical and Information Sciences, Canberra, Australia.
authors.forename: S
authors.initials: S
authors.lastname: Hawkins
authors.forename: G
authors.initials: G
authors.lastname: Williams
authors.forename: R
authors.initials: R
authors.lastname: Baxter
mesh_terms: D001185:Artificial Intelligence; D002984:Clinical Laboratory Information Systems; D019411:Clinical Laboratory Techniques / Q000191:economics / Q000706:statistics & numerical data*; D003625:Data Collection; D019983:Guideline Adherence; D006801:Humans; D009313:National Health Programs / Q000191:economics*; D009517:New South Wales; D010336:Pathology / Q000191:economics / Q000706:statistics & numerical data*; D010818:Practice Patterns, Physicians'