Extracting tumour prognostic factors from a diverse electronic record dataset in genito-urinary oncology. (January 2019)

• Record Type: Journal Article
• Authors: Khor, Richard C.; Nguyen, Anthony; O'Dwyer, John; Kothari, Gargi; Sia, Joseph; Chang, David; Ng, Sweet Ping; Duchesne, Gillian M.; Foroudi, Farshad
• Is Part Of: International journal of medical informatics. Volume 121(2019)
• Page Start: 53
• Page End: 57
• Publication Date: 2019-01
• Subjects: Genitourinary cancers -- Text mining -- Natural language processing -- Electronic medical record -- Tumor staging; Medical informatics -- Periodicals; Information science -- Periodicals; Computers -- Periodicals; Medical technology -- Periodicals; Medical Informatics -- Periodicals; Technology, Medical -- Periodicals; Computers; Information science; Medical informatics; Medical technology; Electronic journals; Periodicals; Electronic journals; 610.285
• DOI: 10.1016/j.ijmedinf.2018.10.008
• Languages: English
• ISSNs: 1386-5056

Abstract

Highlights: A system to extract tumour stage and prognostic data in genito-urinary cancer. A diverse corpus of 1054 notes of pathology, radiology and clinical notes was used. The major advantage is a comprehensive assessment across multiple document types. The performance was equivalent to other simple datasets. Abstract: Objectives: To implement a system for unsupervised extraction of tumor stage and prognostic data in patients with genitourinary cancers using clinicopathological and radiology text. Methods: 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. Results: 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. Conclusions: The Medtex algorithm performed similarly to human annotators for extracting stage and prognostic data from varied clinical texts.