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CodeMagic: Semi-Automatic Assignment of ICD-10-AM Codes to Patient Records

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Information Sciences and Systems 2014

Abstract

In this study, we present a recommendation system for semiautomatic assignment of ICD-10-AM codes to free-text patient records. Only expert annotators can assign codes to medical texts, and the lack of standardization of medical documentation and language specific problems make the assignment process even more challenging. Our system assigns a set of top k ICD codes for each document by exploiting the idea of bag-of-words and by using Lucene search engine and Borda Count voting schema. Before the code assignment task, we preprocess patient records to form query bags. Experiments on a set of clinical records show that promising results are possible for semiautomatic assignment of ICD codes.

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Notes

  1. 1.

    We would like to thank to the Hospital of Ankara Numune Eğitim ve Araştırma and Hacettepe University Hospital.

  2. 2.

    “Patient History,” “Surgery Notes,” “Consultation,” “Patient History,” “Radiology,” “Diagnosis”.

References

  1. K. Crammer, M. Dredze, K. Ganchev, P.P. Talukdar, S. Carroll, Automatic code assignment to medical text, in Proceedings of the Workshop on BioNLP 2007: Biological, Translational and Clinical Language Processing, pp. 129–136 (2007)

    Google Scholar 

  2. N. Lavrac, Selected techniques for data mining in medicine, in Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. 16, p. 23 (1999)

    Google Scholar 

  3. K.J.M. Janssen et al., Missing covariate data in medical research: to impute is better than to ignore. J. Clin. Epidemiol 63, 721–727 (2010)

    Article  Google Scholar 

  4. O. Deniz, Ontology based text mining in Turkish radiology reports (Master’s Thesis, Middle East Technical University, Turkey, 2011)

    Google Scholar 

  5. 2007 International Challenge: Classifying clinical free text using natural language processing

    Google Scholar 

  6. I. Goldstein, A. Arzumtsyan, O. Uzuner, Three approaches to automatic assignment of ICD-9-CM codes to radiology reports, in Proceedings of the Fall Symposium of the American Medical Informatics Association, Chicago, Illinois, USA, American Medical Informatics Association, pp. 279–283 (2007)

    Google Scholar 

  7. R. Farkas, G. Szarvas, Automatic construction of rule-based ICD-9-CM coding systems. BMC Bioinformatics S–3, 9 (2008)

    Google Scholar 

  8. J. Patrick, Y. Zhang, Y. Wang, Developing feature types for classifying clinical notes, in Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing, pp. 191–192 (2007)

    Google Scholar 

  9. S. Boytcheva, Automatic matching of ICD-10 codes to diagnoses in discharge letters, in Proceedings of the Second Workshop on Biomedical Natural Language Processing, pp. 19–26 (2011)

    Google Scholar 

  10. E. Soysal, I. Cicekli, N. Baykal, Design and evaluation of an ontology based information extraction system for radiological reports. Comput. Biol. Med. 40, 900–911 (2010)

    Article  Google Scholar 

  11. A.A. Akin, M.D. Akin, Zemberek, an open source NLP framework for Turkic languages. (2007)

    Google Scholar 

  12. R.B. Rao, S. Sandilya, R.S. Niculescu, Clinical and financial outcomes analysis with existing hospital patient records, in Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 416–425 (2003)

    Google Scholar 

  13. D. Cutting, Lucene search engine. (1999) http://lucene.apache.org/

  14. M.V. Erp, L. Schomaker, Variants of the borda count method for combining ranked classifier hypotheses, in The Seventh Intenational Workshop on Frontiers in Handwriting Recognition, pp. 443–452 (2000)

    Google Scholar 

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Acknowledgments

This project is funded by EES (a software company in Turkey) and TÜBİTAK (Research Council of Turkey) under grant number 3110502.

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Correspondence to Damla Arifoğlu .

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Arifoğlu, D., Deniz, O., Aleçakır, K., Yöndem, M. (2014). CodeMagic: Semi-Automatic Assignment of ICD-10-AM Codes to Patient Records. In: Czachórski, T., Gelenbe, E., Lent, R. (eds) Information Sciences and Systems 2014. Springer, Cham. https://doi.org/10.1007/978-3-319-09465-6_27

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  • DOI: https://doi.org/10.1007/978-3-319-09465-6_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09464-9

  • Online ISBN: 978-3-319-09465-6

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