Multi-task deep neural networks for automated extraction of primary site and laterality information from cancer pathology reports

Hong Jun Yoon; Arvind Ramanathan; Georgia Tourassi

doi:10.1007/978-3-319-47898-2_21

Multi-task deep neural networks for automated extraction of primary site and laterality information from cancer pathology reports

Hong Jun Yoon, Arvind Ramanathan, Georgia Tourassi

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

18 Scopus citations

Abstract

Automated annotation of free-text cancer pathology reports is a critical challenge for cancer registries and the national cancer surveillance program. In this paper, we investigated deep neural networks (DNNs) for automated extraction of the primary cancer site and its laterality, two fundamental targets of cancer reporting. Our experiments showed that single-task DNNs are capable of extracting information with higher precision and recall than traditional classification methods for the more challenging target. Furthermore, a multi-task learning DNN resulted in further performance improvement. This preliminary study, indicate the strong potential for multi-task deep neural networks to extract cancer-relevant information from free-text pathology reports.

Original language	English
Title of host publication	Advances in Big Data - Proceedings of the 2nd INNS Conference on Big Data, 2016
Editors	Asim Roy, Marley Vellasco, Yannis Manolopoulos, Lazaros Iliadis, Plamen Angelov
Publisher	Springer Verlag
Pages	195-204
Number of pages	10
ISBN (Print)	9783319478975
DOIs	https://doi.org/10.1007/978-3-319-47898-2_21
State	Published - 2017
Event	2nd International Neural Network Society Conference on Big Data, INNS 2016 - Thessaloniki, Greece Duration: Oct 23 2016 → Oct 25 2016

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	529
ISSN (Print)	2194-5357

Conference

Conference	2nd International Neural Network Society Conference on Big Data, INNS 2016
Country/Territory	Greece
City	Thessaloniki
Period	10/23/16 → 10/25/16

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 this 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 ). The study was supported by the Laboratory Directed Research and Development (LDRD) program of Oak Ridge National Laboratory, under LDRD projects No. 7417 and No. 8231.

Keywords

Cancer pathology report
Deep neural network
Multi-task learning
Natural language processing

Access to Document

10.1007/978-3-319-47898-2_21

Cite this

Yoon, H. J., Ramanathan, A., & Tourassi, G. (2017). Multi-task deep neural networks for automated extraction of primary site and laterality information from cancer pathology reports. In A. Roy, M. Vellasco, Y. Manolopoulos, L. Iliadis, & P. Angelov (Eds.), Advances in Big Data - Proceedings of the 2nd INNS Conference on Big Data, 2016 (pp. 195-204). (Advances in Intelligent Systems and Computing; Vol. 529). Springer Verlag. https://doi.org/10.1007/978-3-319-47898-2_21

Yoon, Hong Jun ; Ramanathan, Arvind ; Tourassi, Georgia. / Multi-task deep neural networks for automated extraction of primary site and laterality information from cancer pathology reports. Advances in Big Data - Proceedings of the 2nd INNS Conference on Big Data, 2016. editor / Asim Roy ; Marley Vellasco ; Yannis Manolopoulos ; Lazaros Iliadis ; Plamen Angelov. Springer Verlag, 2017. pp. 195-204 (Advances in Intelligent Systems and Computing).

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abstract = "Automated annotation of free-text cancer pathology reports is a critical challenge for cancer registries and the national cancer surveillance program. In this paper, we investigated deep neural networks (DNNs) for automated extraction of the primary cancer site and its laterality, two fundamental targets of cancer reporting. Our experiments showed that single-task DNNs are capable of extracting information with higher precision and recall than traditional classification methods for the more challenging target. Furthermore, a multi-task learning DNN resulted in further performance improvement. This preliminary study, indicate the strong potential for multi-task deep neural networks to extract cancer-relevant information from free-text pathology reports.",

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Yoon, HJ, Ramanathan, A & Tourassi, G 2017, Multi-task deep neural networks for automated extraction of primary site and laterality information from cancer pathology reports. in A Roy, M Vellasco, Y Manolopoulos, L Iliadis & P Angelov (eds), Advances in Big Data - Proceedings of the 2nd INNS Conference on Big Data, 2016. Advances in Intelligent Systems and Computing, vol. 529, Springer Verlag, pp. 195-204, 2nd International Neural Network Society Conference on Big Data, INNS 2016, Thessaloniki, Greece, 10/23/16. https://doi.org/10.1007/978-3-319-47898-2_21

Multi-task deep neural networks for automated extraction of primary site and laterality information from cancer pathology reports. / Yoon, Hong Jun; Ramanathan, Arvind; Tourassi, Georgia.
Advances in Big Data - Proceedings of the 2nd INNS Conference on Big Data, 2016. ed. / Asim Roy; Marley Vellasco; Yannis Manolopoulos; Lazaros Iliadis; Plamen Angelov. Springer Verlag, 2017. p. 195-204 (Advances in Intelligent Systems and Computing; Vol. 529).

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

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AB - Automated annotation of free-text cancer pathology reports is a critical challenge for cancer registries and the national cancer surveillance program. In this paper, we investigated deep neural networks (DNNs) for automated extraction of the primary cancer site and its laterality, two fundamental targets of cancer reporting. Our experiments showed that single-task DNNs are capable of extracting information with higher precision and recall than traditional classification methods for the more challenging target. Furthermore, a multi-task learning DNN resulted in further performance improvement. This preliminary study, indicate the strong potential for multi-task deep neural networks to extract cancer-relevant information from free-text pathology reports.

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Yoon HJ, Ramanathan A, Tourassi G. Multi-task deep neural networks for automated extraction of primary site and laterality information from cancer pathology reports. In Roy A, Vellasco M, Manolopoulos Y, Iliadis L, Angelov P, editors, Advances in Big Data - Proceedings of the 2nd INNS Conference on Big Data, 2016. Springer Verlag. 2017. p. 195-204. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-319-47898-2_21

Multi-task deep neural networks for automated extraction of primary site and laterality information from cancer pathology reports

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