Öz
The fact that some of the symptoms are related to many medical treatment areas causes patients to have difficulty in making an appointment for treatment. In this study, 13 medical fields and 204 symptoms, which are available on the website of many public hospitals associated with T.C. Ministry of Health and used to help patients choose the right medical treatment branch according to their symptoms, were examined using text mining and data science techniques. Based on the content of the text used, the closeness among the medical treatment areas was calculated and the words and symptoms confusing the patients the most while deciding the treatment area were determined. When analyzing the words, meaningless words were ignored, and a word cloud was created on the symptoms of the diseases. In order to calculate the closeness of medical fields, 13x186 binary data was created, indicating whether each symptom exists. Later, the medical fields on this data set were clustered according to the symptoms using agglomerative hierarchical clustering algorithms and the proximity of medical treatment fields was found. In the results, the words that challenge patients the most and the text-based affinities of medical fields are shared. Reorganizing content of the official document used on the hospital websites using the results obtained on this study will help to reduce the number of appointments received from the wrong medical branches.
Anahtar Kelimeler
Hospital appointment Text mining Data science Disease symptoms Health
Kaynakça
- Yavuz, İ. N. A. L., & CAGİLTAY, N. E. E-NABIZ MOBİL SAĞLIK UYGULAMASINA YÖNELİK KULLANICI DEĞERLENDİRMESİ. Hacettepe Sağlık İdaresi Dergisi, 22(2), 375-388.
- Vos, T., Barber, R. M., Bell, B., Bertozzi-Villa, A., Biryukov, S., Bolliger, I., ... & Duan, L. (2015). Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. The Lancet, 386(9995), 743-800.
- Tan, A. H. (1999, April). Text mining: The state of the art and the challenges. In Proceedings of the pakdd 1999 workshop on knowledge disocovery from advanced databases (Vol. 8, pp. 65-70). sn.
- Banerjee, S., Carlin, B. P., & Gelfand, A. E. (2014). Hierarchical modeling and analysis for spatial data. CRC press.
- Aggarwal, C. C., & Zhai, C. (Eds.). (2012). Mining text data. Springer Science & Business Media.
- Kolaczyk, E. D., & Csárdi, G. (2014). Statistical analysis of network data with R (Vol. 65). New York, NY: Springer.
- Karimi, S., Wang, C., Metke-Jimenez, A., Gaire, R., & Paris, C. (2015). Text and data mining techniques in adverse drug reaction detection. ACM Computing Surveys (CSUR), 47(4), 1-39.
- Mittermayer, M. A. (2004, January). Forecasting intraday stock price trends with text mining techniques. In 37th Annual Hawaii International Conference on System Sciences, 2004. Proceedings of the (pp. 10-pp). IEEE.
- Hengstler, M., Enkel, E., & Duelli, S. (2016). Applied artificial intelligence and trust—The case of autonomous vehicles and medical assistance devices. Technological Forecasting and Social Change, 105, 105-120.
- Zang, Y., Zhang, F., Di, C. A., & Zhu, D. (2015). Advances of flexible pressure sensors toward artificial intelligence and health care applications. Materials Horizons, 2(2), 140-156.
- Yu, K. H., & Andrew, L. (2018). Beam, and Isaac S. Kohane. Artificial intelligence in healthcare. Nature biomedical engineering, 2(10), 719-731.
- Kurban, H., Jenne, M., & Dalkilic, M. M. (2017). Using data to build a better EM: EM* for big data. International Journal of Data Science and Analytics, 4(2), 83-97.
- Mohsen, H., Kurban, H., Zimmer, K., Jenne, M., & Dalkilic, M. M. (2015, June). Red-rf: Reduced random forest for big data using priority voting & dynamic data reduction. In 2015 IEEE International Congress on Big Data (pp. 118-125). IEEE.
- UYLAŞ SATI, N. (2018). A collective learning approach for semi-supervised data classification. Pamukkale University Journal of Engineering Sciences, 24(5).
- Buşoniu, L., Babuška, R., & De Schutter, B. (2010). Multi-agent reinforcement learning: An overview. In Innovations in multi-agent systems and applications-1 (pp. 183-221). Springer, Berlin, Heidelberg.
- Jenne, M., Boberg, O., Kurban, H., & Dalkilic, M. (2014). Studying the milky way galaxy using paraheap-k. Computer, 47(9), 26-33.
- Xu, R., & Wunsch, D. (2005). Survey of clustering algorithms. IEEE Transactions on neural networks, 16(3), 645-678.
Öz
Bazı hastalık belirtilerinin birçok tıbbi tedavi alanıyla ilgili olması, hastaların tedavi için randevu alırken zorlanmalarına sebep olmaktadır. Örneğin; karın ağrısı rahatsızlığı bulunan bir hastanın rahatsızlığı dahiliye, hariciye ya da intaniye bölümlerinden herhangi birisiyle ilgisi bulunabilmektedir. Bu çalışmada T.C. Sağlık Bakanlığına bağlı birçok kamu hastanesinin resmî internet sitesinde bulunan ve hastaların belirtilerine göre doğru tıbbi tedavi branşını seçmelerine yardımcı olmak amacıyla kullanılan 13 tıbbi alan ve 204 belirti, metin madenciliği ve veri bilimi teknikleriyle kapsamlı olarak incelenmiştir. Kamu hastanelerinin resmî internet sitelerde kullanılan metnin içeriği baz alınarak tıbbi tedavi alanları arasındaki, yakınlık/uzaklık hesaplanıp, kelime bazlı hastaları randevu alanını belirlerken en çok zorlayan kelimeler ve belirtiler tespit edilmiştir. Kullanılan kelimeler analiz edilirken edat ve bağlaç gibi anlamsız sözcükler göz ardı edilip, hastalık belirtileri üzerinde kelime bulutu (word cloud) oluşturulmuştur. Tıbbi alanların yakınlığını hesaplamak için öncelikle metin içeriği kullanılarak 13 alan için her bir belirtinin var olup olmadığını gösteren 13x186 boyutlu ikili veri (binary data, document matrix) oluşturulmuştur. Daha sonra, bu veri seti üzerinde tıbbi tedavi alanları belirtilere göre aglomeratif hiyerarşik kümeleme algoritmaları (single, complete, average, ward, mcquitty) kullanılarak kümelendirilip metin bazlı alanların birbiri ile yakınlığı tespit edilmiştir. Bu makalenin sonuçlar kısmında hastaları en çok zorlayan kelimeler ve tıbbi alanların metin bazlı yakınlıkları paylaşılmıştır. Elde edilen sonuçlar çerçevesinde kullanılan metnin sağlık uzmanları tarafından tekrar düzenlenmesinin, yanlış tıbbi branşlardan alınan randevu sayısının azaltılmasına katkısı olacaktır.
Anahtar Kelimeler
Hastane randevusu metin madenciliği veri bilimi hastalık belirtileri sağlık
Kaynakça
- Yavuz, İ. N. A. L., & CAGİLTAY, N. E. E-NABIZ MOBİL SAĞLIK UYGULAMASINA YÖNELİK KULLANICI DEĞERLENDİRMESİ. Hacettepe Sağlık İdaresi Dergisi, 22(2), 375-388.
- Vos, T., Barber, R. M., Bell, B., Bertozzi-Villa, A., Biryukov, S., Bolliger, I., ... & Duan, L. (2015). Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. The Lancet, 386(9995), 743-800.
- Tan, A. H. (1999, April). Text mining: The state of the art and the challenges. In Proceedings of the pakdd 1999 workshop on knowledge disocovery from advanced databases (Vol. 8, pp. 65-70). sn.
- Banerjee, S., Carlin, B. P., & Gelfand, A. E. (2014). Hierarchical modeling and analysis for spatial data. CRC press.
- Aggarwal, C. C., & Zhai, C. (Eds.). (2012). Mining text data. Springer Science & Business Media.
- Kolaczyk, E. D., & Csárdi, G. (2014). Statistical analysis of network data with R (Vol. 65). New York, NY: Springer.
- Karimi, S., Wang, C., Metke-Jimenez, A., Gaire, R., & Paris, C. (2015). Text and data mining techniques in adverse drug reaction detection. ACM Computing Surveys (CSUR), 47(4), 1-39.
- Mittermayer, M. A. (2004, January). Forecasting intraday stock price trends with text mining techniques. In 37th Annual Hawaii International Conference on System Sciences, 2004. Proceedings of the (pp. 10-pp). IEEE.
- Hengstler, M., Enkel, E., & Duelli, S. (2016). Applied artificial intelligence and trust—The case of autonomous vehicles and medical assistance devices. Technological Forecasting and Social Change, 105, 105-120.
- Zang, Y., Zhang, F., Di, C. A., & Zhu, D. (2015). Advances of flexible pressure sensors toward artificial intelligence and health care applications. Materials Horizons, 2(2), 140-156.
- Yu, K. H., & Andrew, L. (2018). Beam, and Isaac S. Kohane. Artificial intelligence in healthcare. Nature biomedical engineering, 2(10), 719-731.
- Kurban, H., Jenne, M., & Dalkilic, M. M. (2017). Using data to build a better EM: EM* for big data. International Journal of Data Science and Analytics, 4(2), 83-97.
- Mohsen, H., Kurban, H., Zimmer, K., Jenne, M., & Dalkilic, M. M. (2015, June). Red-rf: Reduced random forest for big data using priority voting & dynamic data reduction. In 2015 IEEE International Congress on Big Data (pp. 118-125). IEEE.
- UYLAŞ SATI, N. (2018). A collective learning approach for semi-supervised data classification. Pamukkale University Journal of Engineering Sciences, 24(5).
- Buşoniu, L., Babuška, R., & De Schutter, B. (2010). Multi-agent reinforcement learning: An overview. In Innovations in multi-agent systems and applications-1 (pp. 183-221). Springer, Berlin, Heidelberg.
- Jenne, M., Boberg, O., Kurban, H., & Dalkilic, M. (2014). Studying the milky way galaxy using paraheap-k. Computer, 47(9), 26-33.
- Xu, R., & Wunsch, D. (2005). Survey of clustering algorithms. IEEE Transactions on neural networks, 16(3), 645-678.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Mühendislik |
| Bölüm | Makaleler |
| Yazarlar | |
| Yayımlanma Tarihi | 31 Ocak 2021 |
| Yayımlandığı Sayı | Yıl 2021 Sayı: 21 |
