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MedSpecSearch: Medical Specialty Search

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11438))

Abstract

MedSpecSearch (www.medspecsearch.com) 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.

This work has been funded by Türk Telekom R&D Center.

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Notes

  1. 1.

    https://www.icliniq.com.

  2. 2.

    https://www.healthtap.com.

  3. 3.

    https://code.google.com/archive/p/word2vec/.

  4. 4.

    http://nlp.stanford.edu/data/glove.6B.zip.

  5. 5.

    http://bio.nlplab.org/.

  6. 6.

    https://github.com/OzU-NLP/MedSpecSearch.

References

  1. Survey of Physician Appointment Wait Times. https://www.merritthawkins.com/news-and-insights/thought-leadership/survey/survey-of-physician-appointment-wait-times/. Accessed 25 Sept 2018

  2. Cohen, J.: A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 20(1), 37–46 (1960)

    Article  Google Scholar 

  3. Doctor visits may cost at least three times as much next year. https://www.thestar.com.my/news/nation/2018/08/27/doctor-visits-may-cost-at-least-three-times-as-much-next-year/. Accessed 25 Sept 2018

  4. Doctors’ visits are about to get more expensive in France. https://www.thelocal.fr/20170915/some-doctors-visits-are-about-to-get-more-expensive-in-france/. Accessed 25 Sept 2018

  5. Johnson, A., et al.: MIMIC-III, a freely accessible critical care database. Sci. Data 3 (2016)

    Article  Google Scholar 

  6. Kim, Y.: Convolutional neural networks for sentence classification. ArXiv e-prints (2014)

    Google Scholar 

  7. Landis, J.R., Koch, G.G.: The measurement of observer agreement for categorical data. Biometrics 33, 159–174 (1977)

    Article  Google Scholar 

  8. Medical Specialties. https://www.aamc.org/cim/specialty/exploreoptions/list/. Accessed 20 Apr 2018

  9. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. ArXiv e-prints (2013)

    Google Scholar 

  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. 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. Sensoy, M., Kandemir, M., Kaplan, L.: Evidential deep learning to quantify classification uncertainty. arXiv preprint arXiv:1806.01768 (2018)

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Correspondence to Reyyan Yeniterzi .

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Şahin, M.U., Balatkan, E., Eran, C., Zeydan, E., Yeniterzi, R. (2019). MedSpecSearch: Medical Specialty Search. In: Azzopardi, L., Stein, B., Fuhr, N., Mayr, P., Hauff, C., Hiemstra, D. (eds) Advances in Information Retrieval. ECIR 2019. Lecture Notes in Computer Science(), vol 11438. Springer, Cham. https://doi.org/10.1007/978-3-030-15719-7_29

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  • DOI: https://doi.org/10.1007/978-3-030-15719-7_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-15718-0

  • Online ISBN: 978-3-030-15719-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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