Natural language processing systems for pathology parsing in limited data environments with uncertainty estimation
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Oct 01, 2020
- Source ID
- 10.1093/jamiaopen/ooaa029
Entities
People
- Anobel Y Odisho
- Bin Yu
- Briton Park
- John Denero
- Matthew R Cooperberg
- Nicholas Altieri
- Peter R. Carroll
Organizations
- Army Research Office
- Chan Zuckerberg Biohub
- National Science Foundation
- Statistics New Zealand
- University of California
- University of California, San Francisco