Published on in Vol 17, No 8 (2015): August

Automatically Detecting Failures in Natural Language Processing Tools for Online Community Text

Automatically Detecting Failures in Natural Language Processing Tools for Online Community Text

Automatically Detecting Failures in Natural Language Processing Tools for Online Community Text

Journals

  1. Park A, Conway M, Chen A. Examining thematic similarity, difference, and membership in three online mental health communities from reddit: A text mining and visualization approach. Computers in Human Behavior 2018;78:98 View
  2. Chen D, Zhang R, Feng J, Liu K. Fulfilling information needs of patients in online health communities. Health Information & Libraries Journal 2020;37(1):48 View
  3. Dirkson , Verberne , Sarker , Kraaij . Data-Driven Lexical Normalization for Medical Social Media. Multimodal Technologies and Interaction 2019;3(3):60 View
  4. Hartzler A, Weis B, Cahill C, Pratt W, Park A, Backonja U, McDonald D. Design and Usability of Interactive User Profiles for Online Health Communities. ACM Transactions on Computer-Human Interaction 2016;23(3):1 View
  5. Zhang S, Bantum E, Owen J, Bakken S, Elhadad N. Online cancer communities as informatics intervention for social support: conceptualization, characterization, and impact. Journal of the American Medical Informatics Association 2017;24(2):451 View
  6. Weissman G, Ungar L, Harhay M, Courtright K, Halpern S. Construct validity of six sentiment analysis methods in the text of encounter notes of patients with critical illness. Journal of Biomedical Informatics 2019;89:114 View
  7. Park A, Conway M. Harnessing Reddit to Understand the Written-Communication Challenges Experienced by Individuals With Mental Health Disorders: Analysis of Texts From Mental Health Communities. Journal of Medical Internet Research 2018;20(4):e121 View
  8. Park A, Conway M. Longitudinal Changes in Psychological States in Online Health Community Members: Understanding the Long-Term Effects of Participating in an Online Depression Community. Journal of Medical Internet Research 2017;19(3):e71 View
  9. Sung S, Chen K, Wu D, Hung L, Su Y, Hu Y. Applying natural language processing techniques to develop a task-specific EMR interface for timely stroke thrombolysis: A feasibility study. International Journal of Medical Informatics 2018;112:149 View
  10. Chiaramello E, Pinciroli F, Bonalumi A, Caroli A, Tognola G. Use of “off-the-shelf” information extraction algorithms in clinical informatics: A feasibility study of MetaMap annotation of Italian medical notes. Journal of Biomedical Informatics 2016;63:22 View
  11. Guetterman T, Chang T, DeJonckheere M, Basu T, Scruggs E, Vydiswaran V. Augmenting Qualitative Text Analysis with Natural Language Processing: Methodological Study. Journal of Medical Internet Research 2018;20(6):e231 View
  12. Wang H, Zeng D. Fusing Logical Relationship Information of Text in Neural Network for Text Classification. Mathematical Problems in Engineering 2020;2020:1 View
  13. Hartzler A, Taylor M, Park A, Griffiths T, Backonja U, McDonald D, Wahbeh S, Brown C, Pratt W. Leveraging cues from person-generated health data for peer matching in online communities. Journal of the American Medical Informatics Association 2016;23(3):496 View
  14. Rudra K, Sharma A, Ganguly N, Imran M. Classifying and Summarizing Information from Microblogs During Epidemics. Information Systems Frontiers 2018;20(5):933 View
  15. VanDam C, Kanthawala S, Pratt W, Chai J, Huh J. Detecting clinically related content in online patient posts. Journal of Biomedical Informatics 2017;75:96 View
  16. Hegde H, Shimpi N, Glurich I, Acharya A. Tobacco use status from clinical notes using Natural Language Processing and rule based algorithm. Technology and Health Care 2018;26(3):445 View