MedSpecSearch: Medical Specialty Search

  • Mehmet Uluç Şahin
  • Eren Balatkan
  • Cihan Eran
  • Engin Zeydan
  • Reyyan YeniterziEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11438)


MedSpecSearch ( is a search engine for helping users to find the relevant medical specialty for a doctor visit based on users’ description of symptoms. This system is useful for users who are not sure of which medical specialty they should consult to. Furthermore, the API of the search engine can be used as part of the online doctor appointment and medical consultation sites to route the patient or question to the right medical specialty. The system returns the top three relevant specialties when the estimated confidence score is high. Otherwise, it asks users to input more data.


Medical text classification Word embeddings Confidence estimation Data collection 


  1. 1.
  2. 2.
    Cohen, J.: A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 20(1), 37–46 (1960)CrossRefGoogle Scholar
  3. 3.
    Doctor visits may cost at least three times as much next year. Accessed 25 Sept 2018
  4. 4.
    Doctors’ visits are about to get more expensive in France. Accessed 25 Sept 2018
  5. 5.
    Johnson, A., et al.: MIMIC-III, a freely accessible critical care database. Sci. Data 3 (2016)CrossRefGoogle Scholar
  6. 6.
    Kim, Y.: Convolutional neural networks for sentence classification. ArXiv e-prints (2014)Google Scholar
  7. 7.
    Landis, J.R., Koch, G.G.: The measurement of observer agreement for categorical data. Biometrics 33, 159–174 (1977)CrossRefGoogle Scholar
  8. 8.
    Medical Specialties. Accessed 20 Apr 2018
  9. 9.
    Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. ArXiv e-prints (2013)Google Scholar
  10. 10.
    Pennington, J., Socher, R., Manning, C.: GloVe: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP, pp. 1532–1543 (2014)Google Scholar
  11. 11.
    Pyysalo, S., Ginter, F., Moen, H., Salakoski, T., Ananiadou, S.: Distributional semantics resources for biomedical text processing. In: Proceedings of the 5th International Symposium on Languages in Biology and Medicine (2013)Google Scholar
  12. 12.
    Sensoy, M., Kandemir, M., Kaplan, L.: Evidential deep learning to quantify classification uncertainty. arXiv preprint arXiv:1806.01768 (2018)

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mehmet Uluç Şahin
    • 1
  • Eren Balatkan
    • 1
  • Cihan Eran
    • 1
  • Engin Zeydan
    • 2
  • Reyyan Yeniterzi
    • 1
    Email author
  1. 1.Özyeğin UniversityIstanbulTurkey
  2. 2.Türk Telekom LabsIstanbulTurkey

Personalised recommendations