Semi-supervised information extraction for cancer pathology reports

John X. Qiu; Shang Gao; Mohammed Alawad; Noah Schaefferkoetter; Folami Alamudun; Hong Jun Yoon; Xiao Cheng Wu; Georgia Tourassi

doi:10.1109/BHI.2019.8834470

Semi-supervised information extraction for cancer pathology reports

John X. Qiu, Shang Gao, Mohammed Alawad, Noah Schaefferkoetter, Folami Alamudun, Hong Jun Yoon, Xiao Cheng Wu, Georgia Tourassi

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

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.

Original language	English
Title of host publication	2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728108483
DOIs	https://doi.org/10.1109/BHI.2019.8834470
State	Published - May 2019
Event	2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Chicago, United States Duration: May 19 2019 → May 22 2019

Publication series

Name	2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings

Conference

Conference	2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019
Country/Territory	United States
City	Chicago
Period	05/19/19 → 05/22/19

Funding

This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of the manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Keywords

Autoencoder
Convolutional neural network
Natural language processing
Semi-supervised learning

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/BHI.2019.8834470

Cite this

Qiu, J. X., Gao, S., Alawad, M., Schaefferkoetter, N., Alamudun, F., Yoon, H. J., Wu, X. C., & Tourassi, G. (2019). Semi-supervised information extraction for cancer pathology reports. In 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings Article 8834470 (2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BHI.2019.8834470

Qiu, John X. ; Gao, Shang ; Alawad, Mohammed et al. / Semi-supervised information extraction for cancer pathology reports. 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. (2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings).

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title = "Semi-supervised information extraction for cancer pathology reports",

abstract = "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.",

keywords = "Autoencoder, Convolutional neural network, Natural language processing, Semi-supervised learning",

author = "Qiu, {John X.} and Shang Gao and Mohammed Alawad and Noah Schaefferkoetter and Folami Alamudun and Yoon, {Hong Jun} and Wu, {Xiao Cheng} and Georgia Tourassi",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 ; Conference date: 19-05-2019 Through 22-05-2019",

year = "2019",

month = may,

doi = "10.1109/BHI.2019.8834470",

language = "English",

series = "2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Qiu, JX, Gao, S, Alawad, M, Schaefferkoetter, N, Alamudun, F, Yoon, HJ, Wu, XC & Tourassi, G 2019, Semi-supervised information extraction for cancer pathology reports. in 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings., 8834470, 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019, Chicago, United States, 05/19/19. https://doi.org/10.1109/BHI.2019.8834470

Semi-supervised information extraction for cancer pathology reports. / Qiu, John X.; Gao, Shang; Alawad, Mohammed et al.
2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. 8834470 (2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Qiu, John X.

AU - Gao, Shang

AU - Alawad, Mohammed

AU - Schaefferkoetter, Noah

AU - Alamudun, Folami

AU - Yoon, Hong Jun

AU - Wu, Xiao Cheng

AU - Tourassi, Georgia

PY - 2019/5

Y1 - 2019/5

N2 - 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.

AB - 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.

KW - Autoencoder

KW - Convolutional neural network

KW - Natural language processing

KW - Semi-supervised learning

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M3 - Conference contribution

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T3 - 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings

BT - 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019

Y2 - 19 May 2019 through 22 May 2019

ER -

Qiu JX, Gao S, Alawad M, Schaefferkoetter N, Alamudun F, Yoon HJ et al. Semi-supervised information extraction for cancer pathology reports. In 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. 8834470. (2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings). doi: 10.1109/BHI.2019.8834470

Semi-supervised information extraction for cancer pathology reports

Abstract

Publication series

Conference

Funding

Keywords

UN SDGs

Access to Document

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