Advertisement

Multi-task Deep Neural Networks for Automated Extraction of Primary Site and Laterality Information from Cancer Pathology Reports

  • Hong-Jun Yoon
  • Arvind Ramanathan
  • Georgia TourassiEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 529)

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.

Keywords

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

Notes

Acknowledgements

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.

References

  1. 1.
    Greenhalgh, T., Hurwitz, B.: Narrative based medicine: why study narrative. Br. Med. J. 318(7175), 48–50 (1999)CrossRefGoogle Scholar
  2. 2.
    Stein, H.D., Nadkarni, P., Erdos, J., Miller, P.L.: Exploring the degree of concordance of coded and textual data in answering clinical queries from a clinical data repository. JAMIA 7(1), 42–54 (2000)Google Scholar
  3. 3.
    Martinez, D., Li, Y.: Information extraction from pathology reports in a hospital setting. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 1877–1882 (2011)Google Scholar
  4. 4.
    Jouhet, V., Defossez, G., Burgun, A., Le Beux, P., Levillain, P., Ingrand, P., Claveau, V.: Automated classification of free-text pathology reports for registration of incident cases of cancer. Methods Inf. Med. 51(3), 242 (2012)CrossRefGoogle Scholar
  5. 5.
    Kavuluru, R., Hands, I., Durbin, E.B., Witt, L.: Automatic extraction of ICD-O-3 primary sites from cancer pathology reports. In: Clinical Research Informatics AMIA Symposium (2013, forthcoming)Google Scholar
  6. 6.
    Bengio, Y., Ducharme, R., Vincent, P., Janvin, C.: A neural probabilistic language model. The. J. Mach. Learn. Res. 3, 1137–1155 (2003)zbMATHGoogle Scholar
  7. 7.
    Collobert, R., Weston, J.: A unified architecture for natural language processing: deep neural networks with multitask learning. In: Proceedings of the 25th International Conference on Machine Learning, pp. 160–167. ACM (2008)Google Scholar
  8. 8.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)Google Scholar
  9. 9.
    Choi, E., Schuetz, A., Stewart, W.F., Sun, J.: Medical concept representation learning from electronic health records and its application on heart failure prediction. arXiv preprint arXiv:1602.03686. (2016)
  10. 10.
    Miotto, R., Li, L., Dudley, J.T.: Deep Learning to predict patient future diseases from the electronic health records. In: Advances in Information Retrieval, pp. 768–774. Springer International Publishing (2016)Google Scholar
  11. 11.
    Caruana, R.: Multitask Learning. Springer, New York (1998)Google Scholar
  12. 12.
    Surveillance, Epidemiology, and End Results (SEER) Program (www.seer.cancer.gov) Research Data (1973–2013), National Cancer Institute, DCCPS, Surveillance Research Program, Surveillance Systems Branch, released April 2016
  13. 13.
    Salton, G., McGill, M.J.: Introduction to Modern Information Retrieval. McGraw-Hill, Inc., New York (1986)Google Scholar
  14. 14.
    Aizawa, A.: An information-theoretic perspective of TF–IDF measures. Inf. Process. Manage. 39(1), 45–65 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Hinton, G., Deng, L., Yu, D., Dahl, G.E., Mohamed, A.R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T.N., Kingsbury, B.: Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Sig. Process. Mag. 29(6), 82–97 (2012)Google Scholar
  16. 16.
    Bottou, L.: Online algorithms and stochastic approximations. In: Saad, D. (ed.) Online Learning and Neural Networks. Cambridge University Press, Cambridge. (1998)Google Scholar
  17. 17.
    Miao, Y.: Kaldi+PDNN: building DNN-based ASR systems with Kaldi and PDNN. arXiv preprint arXiv:1401.6984. (2014)
  18. 18.
    Collobert, R., Weston, J.: A unified architecture for natural language processing: deep neural networks with multitask learning. In: Proceedings of the 25th International Conference on Machine Learning, pp. 160–167. ACM (2008)Google Scholar
  19. 19.
    Team, T.T.D., et al.: Theano: A Python framework for fast computation of mathematical expressions. arXiv preprint arXiv:1605.02688 (2016)
  20. 20.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newslett. 11(1), 10–18 (2009)CrossRefGoogle Scholar
  21. 21.
    ROC Analysis: Web-based Calculator for ROC Curves. http://www.rad.jhmi.edu/jeng/javarad/roc/JROCFITi.html
  22. 22.
    Collobert, R., Kavukcuoglu, K., Farabet, C.: Torch7: A matlab-like environment for machine learning. In: BigLearn, NIPS Workshop, No. EPFL-CONF-192376 (2011)Google Scholar
  23. 23.
    Dean, J., Corrado, G., Monga, R., Chen, K., Devin, M., Mao, M., Ranzato, M., Senior, A., Tucker, P., Yang, K., Ng, A.Y.: Large scale distributed deep networks. In: Advances in Neural Information Processing Systems, pp. 1223–1231 (2012)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Hong-Jun Yoon
    • 1
  • Arvind Ramanathan
    • 1
  • Georgia Tourassi
    • 1
    Email author
  1. 1.Oak Ridge National LaboratoryHealth Data Sciences InstituteOak RidgeUSA

Personalised recommendations