Shoni Colquist

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OBJECTIVE To classify automatically lung tumor-node-metastases (TNM) cancer stages from free-text pathology reports using symbolic rule-based classification. DESIGN By exploiting report substructure and the symbolic manipulation of systematized nomenclature of medicine-clinical terms (SNOMED CT) concepts in reports, statements in free text can be(More)
OBJECTIVE To develop a system for the automatic classification of Cancer Registry notifications data from free-text pathology reports. METHOD The underlying technology used for the extraction of cancer notification items is based on the symbolic rule-based classification methodology, whereby formal semantics are used to reason with the systematised(More)
OBJECTIVE To develop a system for the automatic classification of pathology reports for Cancer Registry notifications. METHOD A two pass approach is proposed to classify whether pathology reports are cancer notifiable or not. The first pass queries pathology HL7 messages for known report types that are received by the Queensland Cancer Registry (QCR),(More)
BACKGROUND Insightful accounts of patient experience within a health care system can be valuable for facilitating improvements in service delivery. OBJECTIVE The aim of this study was to explore patients' perceptions and experiences regarding a tertiary hospital Diabetes and Endocrinology outpatient service for the management of type 2 diabetes mellitus(More)
Cancer Registries record cancer data by reading and interpreting pathology cancer specimen reports. For some Registries this can be a manual process, which is labour and time intensive and subject to errors. A system for automatic extraction of cancer data from HL7 electronic free-text pathology reports has been proposed to improve the workflow efficiency(More)
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