Please use this identifier to cite or link to this item: http://ahro.austin.org.au/austinjspui/handle/1/19959
Title: Extracting tumour prognostic factors from a diverse electronic record dataset in genito-urinary oncology.
Authors: Khor, Richard C;Nguyen, Anthony;O'Dwyer, John;Kothari, Gargi;Sia, Joseph;Chang, David;Ng, Sweet Ping;Duchesne, Gillian M;Foroudi, Farshad
Affiliation: University of Melbourne, Sir Peter MacCallum Department of Oncology, Melbourne, Australia
The Australian e-Health Research Centre, CSIRO, Brisbane, Australia
Department of Radiation Oncology, Austin Health, Heidelberg, Victoria, Australia
Department of Biochemistry, Monash University, Melbourne, Australia
Peter MacCallum Cancer Centre, Department of Radiation Oncology, Melbourne, Australia
Department of Medical Radiations, Monash University, Melbourne, Australia
Department of Cancer Medicine, Latrobe University, Melbourne, Australia
Issue Date: Jan-2019
EDate: 2018-10-23
Citation: International journal of medical informatics 2019; 121: 53-57
Abstract: To implement a system for unsupervised extraction of tumor stage and prognostic data in patients with genitourinary cancers using clinicopathological and radiology text. A corpus of 1054 electronic notes (clinician notes, radiology reports and pathology reports) was annotated for tumor stage, prostate specific antigen (PSA) and Gleason grade. Annotations from five clinicians were reconciled to form a gold standard dataset. A training dataset of 386 documents was sequestered. The Medtex algorithm was adapted using the training dataset. Adapted Medtex equaled or exceeded human performance in most annotations, except for implicit M stage (F-measure of 0.69 vs 0.84) and PSA (0.92 vs 0.96). Overall Medtex performed with an F-measure of 0.86 compared to human annotations of 0.92. There was significant inter-observer variability when comparing human annotators to the gold standard. The Medtex algorithm performed similarly to human annotators for extracting stage and prognostic data from varied clinical texts.
URI: http://ahro.austin.org.au/austinjspui/handle/1/19959
DOI: 10.1016/j.ijmedinf.2018.10.008
ORCID: 0000-0001-8387-0965
PubMed URL: 30545489
Type: Journal Article
Subjects: Electronic medical record
Genitourinary cancers
Natural language processing
Text mining
Tumor staging
Appears in Collections:Journal articles

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