Machine Learning Approaches for Extracting Stage from Pathology Reports in Prostate Cancer. Studies in health technology and informatics Lenain, R., Seneviratne, M. G., Bozkurt, S., Blayney, D. W., Brooks, J. D., Hernandez-Boussard, T. 2019; 264: 1522–23

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.

View details for DOI 10.3233/SHTI190515

View details for PubMedID 31438212