Automatic Generation of Annotated Corpora of Diagnoses with ICD-10 codes based on Open Data and Linked Open Data

• Autorzy: Boytcheva Svetla , Velichkov Boris , Velchev Gerasim , Koychev Ivan

Identyfikatory

DOI

10.15439/2020F192

• Konferencja: Federated Conference on Computer Science and Information Systems (15 ; 06-09.09.2020 ; Sofia, Bulgaria)
• Języki publikacji: EN

Abstrakty

EN

We propose methods for automatic generation of corpora that contains descriptions of diagnoses in Bulgarian and their associated codes in ICD-10-CM (International Classification of Diseases, 10th revision, Clinical Modification). The proposed approach is based on the available open data and Linked Open Data and can be easily adapted for other languages. The resulted corpora generated for the Bulgarian clinical texts consists of about 370,000 pairs of diagnoses and corresponding ICD-10 codes and is beyond the usual size that can be generated manually, moreover it was created from scratch and for a relatively short time. Further updates of the corpora are also possible whenever new open resources are available or the current ones are updated.

Słowa kluczowe

EN

medical computing; natural language processing; text analysis

PL

obliczenia medyczne; przetwarzanie języka naturalnego; analiza tekstu

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2020
• Tom: Vol. 21
• Strony: 163--167
• Opis fizyczny: Bibliogr. 11 poz., il., tab., wykr.

Twórcy

autor

Boytcheva Svetla

• Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria

autor

Velichkov Boris

• Faculty of Mathematics and Informatics, Sofia University "St. Kliment Ohridski", Sofia, Bulgaria

autor

Velchev Gerasim

• Faculty of Mathematics and Informatics, Sofia University "St. Kliment Ohridski", Sofia, Bulgaria

autor

Koychev Ivan

• Faculty of Mathematics and Informatics, Sofia University "St. Kliment Ohridski", Sofia, Bulgaria

Bibliografia

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• 2. S. Boytcheva, "Multilingual aspects of information extraction from medical texts in Bulgarian." Multilingual Processing in Eastern and Southern EU Languages: Less-resourced Technologies and Translation, Cambridge Scholars Publishing. 2012 Apr 25:308-29.
• 3. S. Boytcheva, "Automatic matching of ICD-10 codes to diagnoses in discharge letters."In Proceedings of the second workshop on biomedical natural language processing, RANLP 2011, pp. 11-18, September 2011.
• 4. M. Voinov et al. Latin-Bulgarian Dictionary. Planeta-3, pp. 792, 1999. (in Bulgarian)
• 5. Q. Wang et al. "A study of entity-linking methods for normalizing Chinese diagnosis and procedure terms to ICD codes". Journal of Biomedical Informatics. 2020 Apr 13:103418. https://doi.org/10.1016/j.jbi.2020.103418
• 6. U. Marovac, A. Avdić, D. Janković, and S. Marovac. "Creating Resources for Marking Diagnoses in Electronic Health Reports in Serbian". International Journal of Electrical Engineering and Computing, 2020. 4(1), pp. 18-23.
• 7. M. Almagro, R. M. Unanue, V. Fresno and S. Montalvo, "ICD-10 Coding of Spanish Electronic Discharge Summaries: An Extreme Classification Problem", IEEE Access, 2020, vol. 8, pp. 100073-100083, 2020, http://dx.doi.org/10.1109/ACCESS.2020.2997241.
• 8. A. Bagheri, A. Sammani, PGM Van der Heijden, FW Asselbergs, and DL Oberski. "Automatic ICD-10 classification of diseases from Dutch discharge letters". In: Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: C2C. 2020, pp. 281-289.
• 9. H. Dalianis. "Clinical text retrieval-an overview of basic building blocks and applications". In Professional Search in the Modern World, 2014, pp. 147-165. Springer, Cham.
• 10. J. Wei, and K. Zou. "Eda: Easy data augmentation techniques for boosting performance on text classification tasks". arXiv preprint https://arxiv.org/abs/1901.11196. 2019 Jan 31.
• 11. N. Khairova, S. Petrasova, W. Lewoniewski, O. Mamyrbayev, and K. Mukhsina. "Automatic extraction of synonymous collocation pairs from a text corpus". In 2018 Federated Conference on Computer Science and Information Systems (FedCSIS)". 2018 Sep 9, pp. 485-488, IEEE.

Uwagi

1. Track 1: Artificial Intelligence

2. Technical Session: 5th International Workshop on Language Technologies and Applications

3. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).