This paper is in the following e-collection/theme issue:

Infodemiology and Infoveillance (1351) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1644) Natural Language Processing (694)

Published on in Vol 22, No 10 (2020): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/21383, first published .
Low Testosterone on Social Media: Application of Natural Language Processing to Understand Patients’ Perceptions of Hypogonadism and Its Treatment

Low Testosterone on Social Media: Application of Natural Language Processing to Understand Patients’ Perceptions of Hypogonadism and Its Treatment

Low Testosterone on Social Media: Application of Natural Language Processing to Understand Patients’ Perceptions of Hypogonadism and Its Treatment

Authors of this article:

Vadim Osadchiy1, 2 Author Orcid Image ;   Tommy Jiang1, 2 Author Orcid Image ;   Jesse Nelson Mills1 Author Orcid Image ;   Sriram Venkata Eleswarapu1, 2 Author Orcid Image
Article Authors Cited by (11) Tweetations (11) Metrics

Journals

  1. Sun H, Zhang W, Yu R, Zhang Y. Motion Planning for Mobile Robots—Focusing on Deep Reinforcement Learning: A Systematic Review. IEEE Access 2021;9:69061 View
  2. Tian S, Zhang J, Shu X, Chen L, Niu X, Wang Y. A Novel Evaluation Strategy to Artificial Neural Network Model Based on Bionics. Journal of Bionic Engineering 2022;19(1):224 View
  3. Cummins J, Nambudiri V. Natural language processing: A window to understanding skincare trends. International Journal of Medical Informatics 2022;160:104705 View
  4. Boivin J, Oguz M, Duong M, Cooper O, Filipenko D, Markert M, Samuelsen C, Lenderking W. Emotional reactions to infertility diagnosis: thematic and natural language processing analyses of the 1000 Dreams survey. Reproductive BioMedicine Online 2023;46(2):399 View
  5. Ghayda R, Cannarella R, Calogero A, Shah R, Rambhatla A, Zohdy W, Kavoussi P, Avidor-Reiss T, Boitrelle F, Mostafa T, Saleh R, Toprak T, Birowo P, Salvio G, Calik G, Kuroda S, Kaiyal R, Ziouziou I, Crafa A, Phuoc N, Russo G, Durairajanayagam D, Al-Hashimi M, Hamoda T, Pinggera G, Adriansjah R, Maldonado Rosas I, Arafa M, Chung E, Atmoko W, Rocco L, Lin H, Huyghe E, Kothari P, Solorzano Vazquez J, Dimitriadis F, Garrido N, Homa S, Falcone M, Sabbaghian M, Kandil H, Ko E, Martinez M, Nguyen Q, Harraz A, Serefoglu E, Karthikeyan V, Tien D, Jindal S, Micic S, Bellavia M, Alali H, Gherabi N, Lewis S, Park H, Simopoulou M, Sallam H, Ramirez L, Colpi G, Agarwal A. Artificial Intelligence in Andrology: From Semen Analysis to Image Diagnostics. The World Journal of Men's Health 2024;42(1):39 View
  6. Fruchart M, El Idrissi F, Lamer A, Belarbi K, Lemdani M, Zitouni D, Guinhouya B. Identification of early symptoms of endometriosis through the analysis of online social networks: A social media study. DIGITAL HEALTH 2023;9 View
  7. Sollender G, Jiang T, Finkelshtein I, Osadchiy V, Zheng M, Mills J, Singer J, Eleswarapu S. Understanding Pediatric Experiences With Symptomatic Varicoceles: Mixed Methods Study of an Online Varicocele Community. JMIR Formative Research 2024;8:e50141 View
  8. Naik N, Roth B, Lundy S. Artificial Intelligence for Clinical Management of Male Infertility, a Scoping Review. Current Urology Reports 2025;26(1) View

Books/Policy Documents

  1. Zohdy W, Agarwal A. Current and Future Advances in Male Infertility. View