SavedAdd your journal

Journals / Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi / 2020 / Cilt: 22 - Sayı: 64

Latent Dirichlet Allocation for Medical Dataset

Tıp Veri Kümesi için Gizli Dirichlet Ayrımı

Ekin EKİNCİ
Sevinç İLHAN OMURCA
Elif KIRIK
Şeymanur TAŞÇI
Pages
67–80
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

When the experimental results are analyzed, it has been observed that the titles, which have trend in the last 11 (eleven) years, have been discovered successfully.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Examination of the literature in the relevant field is a very important stage in scientific studies.
📊
Key Findings & Data:The experimental study was carried out on articles in the medical literature in the last 11 (eleven) years from PubMed, which is a medical database based on years.
💡
Conclusion & Impact:When the experimental results are analyzed, it has been observed that the titles, which have trend in the last 11 (eleven) years, have been discovered successfully.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:Konu modelleme · Gizli Dirichlet Ayrımı (GDA) · Tıp literatürü

Abstract

Examination of the literature in the relevant field is a very important stage in scientific studies. When the literature is reviewed manually, it is not possible to perform a comprehensive review or such a search takes a very long time. On the other hand, the automatic search of the literature does not enable in-depth semantic search. In this study, a topic modelling method Latent Dirichlet Allocation (LDA), that performs the automatic and semantic analysis of medical articles published by researchers in Turkey, is applied. The experimental study was carried out on articles in the medical literature in the last 11 (eleven) years from PubMed, which is a medical database based on years. When the experimental results are analyzed, it has been observed that the titles, which have trend in the last 11 (eleven) years, have been discovered successfully.

Özet

Bilimsel çalışmalarda ilgili alandaki literatürün incelenmesi oldukça önemli bir aşamadır. Literatür insan tarafından tarandığında, geniş kapsamlı bir inceleme yapılması mümkün olamamakta, ya da böyle bir arama çok uzun zaman almaktadır. Öte yandan literatürün otomatik olarak taranması derinlemesine bir anlamsal analizi mümkün kılmamaktadır. Bu çalışma kapsamında Türkiye’deki araştırmacılar tarafından yayınlanmış tıp makalelerinin otomatik ve anlamsal analizini gerçekleştiren bir konu modelleme yöntemi olan Gizli Dirichlet Ayrımı (GDA) uygulanmıştır. Deneysel çalışma, yıllara göre bir tıp veritabanı olan PubMed’den elde edilen son 11 (on bir) yıldaki yayınla tıp literatüründeki makaleler üzerinde gerçeklenmiştir. Deneysel sonuçlar incelendiğinde, son 11 (on bir) yılda trend olan çalışma başlıklarının başarılı bir şekilde keşfedildiği gözlenmiştir.

🔖

Cite This Article

EKİNCİ, E., OMURCA, S. İ., KIRIK, E., TAŞÇI, Ş. (2020). Latent Dirichlet Allocation for Medical Dataset. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi, Cilt: 22 - Sayı: 64, 67–80. https://ajindex.com/articles/156623

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (14)

  1. Blei, D.M., Ng, A.Y. 2003.
  2. Latent dirichlet allocation, The Journal of Machine Learning Research, Cilt. 3, s. 993-1022.
  3. Agrawal, A., Fu, W., Menzies, T. 2018. What is wrong with topic modeling? And how to fix it using search-based software engineering, Information and Software Technology, Cilt. 98, s. 74-88. DOI: 10.1016/j.infsof.2018.02.005 Holzinger, A., Dehmer, M., Jurisica, I. 2014. Knowledge discovery and interactive data mining in bioinformatics: State-of-the-art, future challenges and research directions, BMC Bioinformatics, Cilt. 15, s. 1-9. DOI: 10.1186/1471-2105-15-S6-I1 Lin, J.M., Bohland, J.V., Andrews, P., Burns, G.A.P.C., Allen, C.B., Mitra, P.P. 2008. An Analysis of the Abstracts Presented at the Annual Meetings of the Society for Neuroscience from 2001 to 2006,
  4. PLoS One, Cilt. 3, s. 1-9. DOI: 10.1371/journal.pone.0002052
  5. Bundschus, M., Tresp, V., Kriegel, H.P. 2009. Topic Models for Semantically Annotated Document Collections. NIPS Workshop: Applications for Topic Models: Text and Beyond, 7-10
  6. Aralık, Whistler, 1-4.
  7. Jiang, Z., Zhou, X., Zhang, X., Chen, S. 2012. Using Link Topic Model to Analyze Traditional Chinese Medicine Clinical Symptom-Herb Regularities. 2012 IEEE 14th International Conference on e-Health Networking, Applications and Services (Healthcom), 10-13
  8. Ekim, Pekin, 15-18.
  9. Redfield, C.K., Lou, X., Karaletsos, T., Crosbie, C., Gardos, S., Artz, D., Ratsch, G. 2013. An empirical analysis of topic modeling for mining cancer clinical notes. 2013 IEEE 13th International Conference on Data Mining Workshops, 7-10
  10. Aralık, Dallas, 56-63.
  11. Cui, M., Liang, Y., Li, Y., Guan, R. 2015. Exploring Trends of Cancer Research Based on Topic Model. 1st International Workshop on Semantic Technologies, 9-12
  12. Mart, Changchun, 7-18.
  13. Beykikhoshk, A., Arandjelović, O., Venkatesh, S., Phung, D. 2015. Hierarchical Dirichlet Process for Tracking Complex Topical Structure Evolution and Its Application to Autism Research Literature. ss 550-562.
  14. Cao, T., Lim, E.P., Zhou, Z.H., Ho, T.B., Cheung, D., Motoda, H., ed. 2015. Advances in Knowledge Discovery and Data Mining, Springer Cham, Switzerland, 763s. Song, M., Heo, G.E., Lee, D. 2015. Identifying the landscape of Alzheimer’s disease research with network and content analysis, Scientometrics, Cilt. 102, s. 905-927. DOI: 10.1007/s11192-014-1372-x van Altena, A.J., Moerland, P.D., Zwinderman, A.H., Olabarriaga, S.D. 2016. Understanding big data themes from scientific biomedical literature through topic modeling,