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Detecting Automatic Patterns of Stroke Through Text Mining

  • Miguel Vieira
  • Filipe PortelaEmail author
  • Manuel Filipe Santos
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 273)

Abstract

Despite the volume increase of electronic data collection in the health area, there is still much medical information that is recorded without any systematic pattern. For instance, besides the structured admission notes format, there are free text fields for clinicians’ patient evaluation observation. Intelligent Decisions Support Systems can benefit from cross-referencing and interpretation of these documents. In the Intensive Care Units, several patients are admitted daily, and several discharge notes are written. To support real-time decision-making and to increase the quality of its process, is crucial to have all relevant patient clinical data available. Since there is no writing pattern followed by all medical doctors, its analysis becomes quite difficult to do. This project aims to make qualitatively and quantitatively analysis of clinical information focusing on the stroke or cerebrovascular accident diagnosis using text analysis tools, namely Natural Language Processing and Text Mining. Our results revealed a set of related words in the clinician’ patient diaries that can reveal patterns.

Keywords

Medical information Admission notes Intelligent Decisions Support Systems Intensive Care Units 

Notes

Acknowledgements

This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013. This work is also supported by the Deus ex Machina (DEM): Symbiotic technology for societal efficiency gains - NORTE-01-0145-FEDER-000026.

References

  1. 1.
    Chowdhury, G.: Natural language processing. Annual Review of this is an author-produced version of a paper published in The Annual Review of Information Science and Technology ISSN 0066–4200. This version has been peer-reviewed, but does not. Annu. Rev. Inf. Sci. Technol. 37, 51–89 (2003)CrossRefGoogle Scholar
  2. 2.
    Aw, P.: Medicine, computers, and linguistics. Adv. Biomed. Eng. 3, 97–140 (1973)Google Scholar
  3. 3.
    Sager, N., Lyman, M., Bucknall, C., Nhan, N., Tick, L.J.: Natural language processing and the representation of clinical data. J. Am. Med. Inform. Assoc. 1(2), 142–160 (1994)CrossRefGoogle Scholar
  4. 4.
    SNOMED: SNOMED International, no. Dec 2010 (2006)Google Scholar
  5. 5.
    Tan, A.-H.: Text mining: the state of the art and the challenges. In: Proceedings of PAKDD 1999 Workshop on Knowledge Discovery from Advanced Databases, vol. 8, pp. 65–70 (1999)Google Scholar
  6. 6.
    Truyens, M., Van Eecke, P.: Legal aspects of text mining. Comput. Law Secur. Rev. 30(2), 153–170 (2014)CrossRefGoogle Scholar
  7. 7.
    Zhao, Y.: Text mining. In: R Data Mining, pp. 105–122 (2013)Google Scholar
  8. 8.
    Feldman, R., Dagan, I.: Knowledge discovery in textual databases (KDT). In: International Conference on Knowledge Discovery and Data Mining, pp. 112–117 (1995)Google Scholar
  9. 9.
    Pedi, C.I.: Critérios de admissão no Serviço de Cuidados Intensivos Pediátricos. pp. 2–3 (2014)Google Scholar
  10. 10.
    Part I: General Considerations, the Epidemiologic Transition: Clinical Cardiology : New Frontiers Global Burden of Cardiovascular Diseases, no. C (2001)Google Scholar
  11. 11.
    De Magalhães, R., De Oliveira, C., Augusto, L., De Andrade, F.: Artigos Acidente vascular cerebral, vol. 8, no. 3, pp. 280–290 (2001)Google Scholar
  12. 12.
    Alves, C.: Determinantes da capacidade funcional do doente após acidente vascular cerebral (2011)Google Scholar
  13. 13.
    Gago, P., Santos, M.F., Silva, A., Cortez, P., Neves, J., Gomes, L.: INTCare: a knowledge discovery based intelligent decision support system for intensive care medicine. J. Decis. Syst. 14(3), 241–259 (2005)CrossRefGoogle Scholar
  14. 14.
    Kocbek, P., Fijačko, N., Zorman, M., Kocbek, S., Štiglic, G.: Improving mortality prediction for intensive care unit patients using text mining techniques, pp. 2–5 (2012)Google Scholar
  15. 15.
    Gowri, S., Anandha Mala, G.S.: Efficacious IR system for investigation in digital textual data. Indian J. Sci. Technol. 8(12), 43102 (2015)Google Scholar
  16. 16.
    Higuchi, K.: KH coder. Ref. Man. 99 (2016). http://khcoder.net/en/manual_en_v2.pdf

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  • Miguel Vieira
    • 1
  • Filipe Portela
    • 1
    Email author
  • Manuel Filipe Santos
    • 1
  1. 1.Algoritmi Research CenterUniversity of MinhoGuimarãesPortugal

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