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Title: Semi-Supervised Information Extraction for Cancer Pathology Reports

Conference ·
 [1];  [1]; ORCiD logo [1];  [1]; ORCiD logo [1]; ORCiD logo [1];  [2]; ORCiD logo [1]
  1. ORNL
  2. LSUHSC-Louisiana Tumor Registry
+ Show Author Affiliations

Pathology reports are a main source of data for cancer surveillance programs. Manual coding of pathology reports is labor-intensive but necessary for obtaining labeled data to train automated information extraction systems. In this study, we investigated semi-supervised deep learning, improving the performance of a multitask information extraction system for automated annotation of pathology reports. We used a set of over 374,000 pathology reports from the Louisiana Tumor Registry and a novel convolutional attention-based auto-encoder. We performed a set of experiments comparing supervised training augmented with unlabeled data at 1%, 5%, 10%, and 50% of the original data size. We also compared the impact of extending text processing to include unlabeled tokens. We find that semi-supervised training consistently improved individual performance with increased micro-averaged F-scores between 0.012 and 0.064 and increased macro-averaged F-scores of up to 0.158. This demonstrates that semantic information learned via unsupervised learning can be used to improve supervised clinical task performance.

Research Organization:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Organization:
USDOE
DOE Contract Number:
AC05-00OR22725
OSTI ID:
1564225
Resource Relation:
Conference: IEEE EMBS International Conference on Biomedical & Health Informatics (IEEE-EMBS BHI 2019) - Chicago, Illinois, United States of America - 5/19/2019 8:00:00 AM-5/22/2019 8:00:00 AM
Country of Publication:
United States
Language:
English

References (5)

Hierarchical attention networks for information extraction from cancer pathology reports journal November 2017
Scalable deep text comprehension for Cancer surveillance on high-performance computing journal December 2018
Explainable Prediction of Medical Codes from Clinical Text
  • Mullenbach, James; Wiegreffe, Sarah; Duke, Jon
  • Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) https://doi.org/10.18653/v1/N18-1100
conference January 2018
Deep learning journal May 2015
Natural Language Processing in Medicine: An Overview journal September 1996

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