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Case-Based Interpretation of Best Medical Coding Practices—Application to Data Collection for Cancer Registries

  • Michael SchnellEmail author
  • Sophie Couffignal
  • Jean Lieber
  • Stéphanie Saleh
  • Nicolas Jay
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10339)

Abstract

Cancer registries are important tools in the fight against cancer. At the heart of these registries is the data collection and coding process. Ruled by complex international standards and numerous best practices, operators are easily overwhelmed. In this paper, a system is presented to assist operators in the interpretation of best medical coding practices. By leveraging the arguments used by the coding experts to determine the best coding option, the proposed system is designed to answer the coding questions from operators and provide an answer associated with a partial explanation for the proposed solution.

Keywords

Interpretation of best practices Interpretive case-based reasoning Coding standards Cancer registries User assistance Decision support 

Notes

Acknowledgments

The authors wish to thank the anonymous reviewers for their remarks which have helped in improving the quality of the paper. The first author would also like to thank the Fondation Cancer for their financial support.

References

  1. 1.
    Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Commun. 7, 39–59 (1994)Google Scholar
  2. 2.
    Aleven, V., Ashley, K.D.: Teaching case-based argumentation through a model and examples empirical evaluation of an intelligent learning environment. In: Artificial intelligence in education, vol. 39, pp. 87–94 (1997)Google Scholar
  3. 3.
    Ashley, K.D.: Modeling Legal Arguments: Reasoning with Cases and Hypotheticals. MIT Press, Cambridge (1991)Google Scholar
  4. 4.
    Bichindaritz, I., Marling, C., Montani, S.: Case-based reasoning in the health sciences. In: Workshop Proceedings of ICCBR (2015)Google Scholar
  5. 5.
    Brickley, D., Guha, R.V.: RDF Schema 1.1, W3C recommendation (2014). https://www.w3.org/TR/rdf-schema/. Accessed Mar 2017
  6. 6.
    Bunke, H., Messmer, B.T.: Similarity measures for structured representations. In: Wess, S., Althoff, K.-D., Richter, M.M. (eds.) EWCBR 1993. LNCS, vol. 837, pp. 106–118. Springer, Heidelberg (1994). doi: 10.1007/3-540-58330-0_80 CrossRefGoogle Scholar
  7. 7.
    Cardoso, S.D., Pruski, C., Silveira, M., Lin, Y.-C., Groß, A., Rahm, E., Reynaud-Delaître, C.: Leveraging the impact of ontology evolution on semantic annotations. In: Blomqvist, E., Ciancarini, P., Poggi, F., Vitali, F. (eds.) EKAW 2016. LNCS, vol. 10024, pp. 68–82. Springer, Cham (2016). doi: 10.1007/978-3-319-49004-5_5 CrossRefGoogle Scholar
  8. 8.
    Crammer, K., Dredze, M., Ganchev, K., Talukdar, P.P., Carroll, S.: Automatic code assignment to medical text. In: Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing, pp. 129–136. Association for Computational Linguistics (2007)Google Scholar
  9. 9.
    European Network of Cancer Registries, Tyczynski, J.E., Démaret, D., Parkin, D.M.: Standards and guidelines for cancer registration in Europe: the ENCR recommendations. International Agency for Research on Cancer (2003)Google Scholar
  10. 10.
    Kavuluru, R., Han, S., Harris, D.: Unsupervised extraction of diagnosis codes from EMRs using knowledge-based and extractive text summarization techniques. In: Zaïane, O.R., Zilles, S. (eds.) AI 2013. LNCS, vol. 7884, pp. 77–88. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-38457-8_7 CrossRefGoogle Scholar
  11. 11.
    Kavuluru, R., Hands, I., Durbin, E.B., Witt, L.: Automatic Extraction of ICD-O-3 Primary Sites from Cancer Pathology Reports (2013). http://www.ncbi.nlm.nih.gov/pmc/papers/PMC3845766/
  12. 12.
    Maximini, K., Maximini, R., Bergmann, R.: An investigation of generalized cases. In: Ashley, K.D., Bridge, D.G. (eds.) ICCBR 2003. LNCS, vol. 2689, pp. 261–275. Springer, Heidelberg (2003). doi: 10.1007/3-540-45006-8_22 CrossRefGoogle Scholar
  13. 13.
    McSherry, D.: Explaining the pros and cons of conclusions in CBR. In: Funk, P., González Calero, P.A. (eds.) ECCBR 2004. LNCS, vol. 3155, pp. 317–330. Springer, Heidelberg (2004). doi: 10.1007/978-3-540-28631-8_24 CrossRefGoogle Scholar
  14. 14.
    Murdock, J.W., Aha, D.W., Breslow, L.A.: Assessing elaborated hypotheses: an interpretive case-based reasoning approach. In: Ashley, K.D., Bridge, D.G. (eds.) ICCBR 2003. LNCS, vol. 2689, pp. 332–346. Springer, Heidelberg (2003). doi: 10.1007/3-540-45006-8_27 CrossRefGoogle Scholar
  15. 15.
    Pestian, J.P., Brew, C., Matykiewicz, P., Hovermale, D.J., Johnson, N., Cohen, K.B., Duch, W.: A shared task involving multi-label classification of clinical free text. In: Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing, pp. 97–104. Association for Computational Linguistics (2007). 1572411Google Scholar
  16. 16.
    Richter, M.M., Weber, R.O.: Case-Based Reasoning: A Textbook. Springer Science & Business Media, Berlin (2013)CrossRefGoogle Scholar
  17. 17.
    Smyth, B., Keane, M.T.: Remembering to forget. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence, pp. 377–382. Citeseer (1995)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Michael Schnell
    • 1
    • 2
    Email author
  • Sophie Couffignal
    • 1
  • Jean Lieber
    • 2
  • Stéphanie Saleh
    • 1
  • Nicolas Jay
    • 2
    • 3
  1. 1.Epidemiology and Public Health Research Unit, Department of Population HealthLuxembourg Institute of HealthStrassenLuxembourg
  2. 2.UL, CNRS, Inria, LoriaNancyFrance
  3. 3.Service d’évaluation et d’information médicales, Centre Hospitalier Régional Universitaire de NancyNancyFrance

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