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Title: Automatic extraction of cancer registry reportable information from free-text pathology reports using multitask convolutional neural networks

Abstract

Objective: In this work we implement 2 different multitask learning (MTL) techniques, hard parameter sharing and cross-stitch, to train a word-level convolutional neural network (CNN) specifically designed for automatic extraction of cancer data from unstructured text in pathology reports. We show the importance of learning related information extraction (IE) tasks leveraging shared representations across the tasks to achieve state-of-the-art performance in classification accuracy and computational efficiency. Materials and Methods: Multitask CNN (MTCNN) attempts to tackle document information extraction by learning to extract multiple key cancer characteristics simultaneously. We trained our MTCNN to perform 5 information extraction tasks: (1) primary cancer site (65 classes), (2) laterality (4 classes), (3) behavior (3 classes), (4) histological type (63 classes), and (5) histological grade (5 classes). We evaluated the performance on a corpus of 95 231 pathology documents (71 223 unique tumors) obtained from the Louisiana Tumor Registry. We compared the performance of the MTCNN models against single-task CNN models and 2 traditional machine learning approaches, namely support vector machine (SVM) and random forest classifier (RFC). Results: MTCNNs offered superior performance across all 5 tasks in terms of classification accuracy as compared with the other machine learning models. Based on retrospective evaluation, the hard parametermore » sharing and cross-stitch MTCNN models correctly classified 59.04% and 57.93% of the pathology reports respectively across all 5 tasks. The baseline models achieved 53.68% (CNN), 46.37% (RFC), and 36.75% (SVM). Based on prospective evaluation, the percentages of correctly classified cases across the 5 tasks were 60.11% (hard parameter sharing), 58.13% (cross-stitch), 51.30% (single-task CNN), 42.07% (RFC), and 35.16% (SVM). Moreover, hard parameter sharing MTCNNs outperformed the other models in computational efficiency by using about the same number of trainable parameters as a single-task CNN. Conclusions: The hard parameter sharing MTCNN offers superior classification accuracy for automated coding support of pathology documents across a wide range of cancers and multiple information extraction tasks while maintaining similar training and inference time as those of a single task–specific model.« less

Authors:
 [1];  [1];  [1];  [1];  [1];  [2];  [3];  [3];  [4];  [1]
+ Show Author Affiliations
  1. Computational Sciences and Engineering Division, Health Data Sciences Institute, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
  2. Surveillance Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, Maryland, USA
  3. Louisiana Tumor Registry, Louisiana State University Health Sciences Center School of Public Health, New Orleans, Louisiana, USA
  4. Information Management Services Inc, Calverton, Maryland, USA
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF); Argonne National Lab. (ANL), Argonne, IL (United States); Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
National Cancer Institute (NCI); National Institutes of Health (NIH); USDOE Office of Science (SC)
OSTI Identifier:
1574032
Alternate Identifier(s):
OSTI ID: 1607171
Grant/Contract Number:  
AC05-00OR22725; AC02-06CH11357; AC52-07NA27344; AC52-06NA25396
Resource Type:
Published Article
Journal Name:
Journal of the American Medical Informatics Association
Additional Journal Information:
Journal Name: Journal of the American Medical Informatics Association; Journal ID: ISSN 1067-5027
Publisher:
Oxford University Press
Country of Publication:
United Kingdom
Language:
English
Subject:
96 KNOWLEDGE MANAGEMENT AND PRESERVATION

Citation Formats

Alawad, Mohammed, Gao, Shang, Qiu, John X., Yoon, Hong Jun, Blair Christian, J., Penberthy, Lynne, Mumphrey, Brent, Wu, Xiao-Cheng, Coyle, Linda, and Tourassi, Georgia. Automatic extraction of cancer registry reportable information from free-text pathology reports using multitask convolutional neural networks. United Kingdom: N. p., 2019. Web. doi:10.1093/jamia/ocz153.
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Alawad, Mohammed, Gao, Shang, Qiu, John X., Yoon, Hong Jun, Blair Christian, J., Penberthy, Lynne, Mumphrey, Brent, Wu, Xiao-Cheng, Coyle, Linda, & Tourassi, Georgia. Automatic extraction of cancer registry reportable information from free-text pathology reports using multitask convolutional neural networks. United Kingdom. https://doi.org/10.1093/jamia/ocz153
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Alawad, Mohammed, Gao, Shang, Qiu, John X., Yoon, Hong Jun, Blair Christian, J., Penberthy, Lynne, Mumphrey, Brent, Wu, Xiao-Cheng, Coyle, Linda, and Tourassi, Georgia. Sat . "Automatic extraction of cancer registry reportable information from free-text pathology reports using multitask convolutional neural networks". United Kingdom. https://doi.org/10.1093/jamia/ocz153.
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@article{osti_1574032,
title = {Automatic extraction of cancer registry reportable information from free-text pathology reports using multitask convolutional neural networks},
author = {Alawad, Mohammed and Gao, Shang and Qiu, John X. and Yoon, Hong Jun and Blair Christian, J. and Penberthy, Lynne and Mumphrey, Brent and Wu, Xiao-Cheng and Coyle, Linda and Tourassi, Georgia},
abstractNote = {Objective: In this work we implement 2 different multitask learning (MTL) techniques, hard parameter sharing and cross-stitch, to train a word-level convolutional neural network (CNN) specifically designed for automatic extraction of cancer data from unstructured text in pathology reports. We show the importance of learning related information extraction (IE) tasks leveraging shared representations across the tasks to achieve state-of-the-art performance in classification accuracy and computational efficiency. Materials and Methods: Multitask CNN (MTCNN) attempts to tackle document information extraction by learning to extract multiple key cancer characteristics simultaneously. We trained our MTCNN to perform 5 information extraction tasks: (1) primary cancer site (65 classes), (2) laterality (4 classes), (3) behavior (3 classes), (4) histological type (63 classes), and (5) histological grade (5 classes). We evaluated the performance on a corpus of 95 231 pathology documents (71 223 unique tumors) obtained from the Louisiana Tumor Registry. We compared the performance of the MTCNN models against single-task CNN models and 2 traditional machine learning approaches, namely support vector machine (SVM) and random forest classifier (RFC). Results: MTCNNs offered superior performance across all 5 tasks in terms of classification accuracy as compared with the other machine learning models. Based on retrospective evaluation, the hard parameter sharing and cross-stitch MTCNN models correctly classified 59.04% and 57.93% of the pathology reports respectively across all 5 tasks. The baseline models achieved 53.68% (CNN), 46.37% (RFC), and 36.75% (SVM). Based on prospective evaluation, the percentages of correctly classified cases across the 5 tasks were 60.11% (hard parameter sharing), 58.13% (cross-stitch), 51.30% (single-task CNN), 42.07% (RFC), and 35.16% (SVM). Moreover, hard parameter sharing MTCNNs outperformed the other models in computational efficiency by using about the same number of trainable parameters as a single-task CNN. Conclusions: The hard parameter sharing MTCNN offers superior classification accuracy for automated coding support of pathology documents across a wide range of cancers and multiple information extraction tasks while maintaining similar training and inference time as those of a single task–specific model.},
doi = {10.1093/jamia/ocz153},
journal = {Journal of the American Medical Informatics Association},
number = ,
volume = ,
place = {United Kingdom},
year = {2019},
month = {11}
}
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https://doi.org/10.1093/jamia/ocz153
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