Machine Learning Approaches for Extracting Stage from Pathology Reports in Prostate Cancer
Raphael Lenain1, Martin G Seneviratne1, Selen Bozkurt1,2
1Department of Medicine, Biomedical Informatics, Stanford University, Stanford, CA, USA.
Abstract:
Clinical and pathological stage are defining parameters in oncology, which direct a patient's treatment options and prognosis. Pathology reports contain a wealth of staging information that is not stored in structured form in most electronic health records (EHRs). Therefore, we evaluated three supervised machine learning methods (Support Vector Machine, Decision Trees, Gradient Boosting) to classify free-text pathology reports for prostate cancer into T, N and M stage groups.
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