Data from: Natural language processing systems for pathology parsing in limited data environments with uncertainty estimation

Anobel Odisho, Briton Park, Nicholas Altieri, John DeNero, Matthew Cooperberg, Peter Carroll & Bin Yu
Objective: Cancer is a leading cause of death, but much of the diagnostic information is stored as unstructured data in pathology reports. We aim to improve uncertainty estimates of machine-learning based pathology parsers and evaluate performance in low data settings. Materials and Methods: Our data comes from the Urologic Outcomes Database at UCSF which includes 3,232 annotated prostate cancer pathology reports from 2001-2018. We approach 17 separate information extraction tasks, involving a wide range of...
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