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

Natural Language Processing (706) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1666) Social Media for Diabetes (35)

Published on in Vol 4, No 4 (2016): Oct-Dec

This is a member publication of Florida State University

Consumers’ Use of UMLS Concepts on Social Media: Diabetes-Related Textual Data Analysis in Blog and Social Q&A Sites

Consumers’ Use of UMLS Concepts on Social Media: Diabetes-Related Textual Data Analysis in Blog and Social Q&A Sites

Consumers’ Use of UMLS Concepts on Social Media: Diabetes-Related Textual Data Analysis in Blog and Social Q&A Sites

Authors of this article:

Min Sook Park1 Author Orcid Image ;   Zhe He1, 2 Author Orcid Image ;   Zhiwei Chen3 Author Orcid Image ;   Sanghee Oh1 Author Orcid Image ;   Jiang Bian4 Author Orcid Image
Article Authors Cited by (22) Tweetations (4) Metrics

Journals

  1. Amith M, He Z, Bian J, Lossio-Ventura J, Tao C. Assessing the practice of biomedical ontology evaluation: Gaps and opportunities. Journal of Biomedical Informatics 2018;80:1 View
  2. He Z, Chen Z, Oh S, Hou J, Bian J. Enriching consumer health vocabulary through mining a social Q&A site: A similarity-based approach. Journal of Biomedical Informatics 2017;69:75 View
  3. Park M, Oh H, You S. Health information seeking among people with multiple chronic conditions: Contextual factors and their associations mined from questions in social media. Library & Information Science Research 2020;42(3):101030 View
  4. Alasmari A, Zhou L. How multimorbid health information consumers interact in an online community Q&A platform. International Journal of Medical Informatics 2019;131:103958 View
  5. Bahng J, Lee C. Topic Modeling for Analyzing Patients’ Perceptions and Concerns of Hearing Loss on Social Q&A Sites: Incorporating Patients’ Perspective. International Journal of Environmental Research and Public Health 2020;17(17):6209 View
  6. Gabarron E, Bradway M, Fernandez-Luque L, Chomutare T, Hansen A, Wynn R, Årsand E. Social media for health promotion in diabetes: study protocol for a participatory public health intervention design. BMC Health Services Research 2018;18(1) View
  7. Vasilevsky N, Foster E, Engelstad M, Carmody L, Might M, Chambers C, Dawkins H, Lewis J, Della Rocca M, Snyder M, Boerkoel C, Rath A, Terry S, Kent A, Searle B, Baynam G, Jones E, Gavin P, Bamshad M, Chong J, Groza T, Adams D, Resnick A, Heath A, Mungall C, Holm I, Rageth K, Brownstein C, Shefchek K, McMurry J, Robinson P, Köhler S, Haendel M. Plain-language medical vocabulary for precision diagnosis. Nature Genetics 2018;50(4):474 View
  8. Alasmari A, Zhou L. Share to Seek: The Effects of Disease Complexity on Health Information–Seeking Behavior. Journal of Medical Internet Research 2021;23(3):e21642 View
  9. Zhang Z, Citardi D, Xing A, Luo X, Lu Y, He Z. Patient Challenges and Needs in Comprehending Laboratory Test Results: Mixed Methods Study. Journal of Medical Internet Research 2020;22(12):e18725 View
  10. Alarifi M, Patrick T, Jabour A, Wu M, Luo J. Understanding patient needs and gaps in radiology reports through online discussion forum analysis. Insights into Imaging 2021;12(1) View
  11. Kamba M, Manabe M, Wakamiya S, Yada S, Aramaki E, Odani S, Miyashiro I. Medical Needs Extraction for Breast Cancer Patients from Question and Answer Services: Natural Language Processing-Based Approach. JMIR Cancer 2021;7(4):e32005 View
  12. Turki H, Jemielniak D, Hadj Taieb M, Labra Gayo J, Ben Aouicha M, Banat M, Shafee T, Prud’hommeaux E, Lubiana T, Das D, Mietchen D. Using logical constraints to validate statistical information about disease outbreaks in collaborative knowledge graphs: the case of COVID-19 epidemiology in Wikidata. PeerJ Computer Science 2022;8:e1085 View
  13. Tong C, Margolin D, Chunara R, Niederdeppe J, Taylor T, Dunbar N, King A. Search Term Identification Methods for Computational Health Communication: Word Embedding and Network Approach for Health Content on YouTube. JMIR Medical Informatics 2022;10(8):e37862 View
  14. Chang E. A vector-based semantic relatedness measure using multiple relations within SNOMED CT and UMLS. Journal of Biomedical Informatics 2022;131:104118 View
  15. Kariya A, Okada H, Suzuki S, Dote S, Nishikawa Y, Araki K, Takahashi Y, Nakayama T. Internet-Based Inquiries From Users With the Intention to Overdose With Over-the-Counter Drugs: Qualitative Analysis of Yahoo! Chiebukuro. JMIR Formative Research 2023;7:e45021 View
  16. Hakariya H, Yokoyama N, Lee J, Hakariya A, Ikejiri T. Illicit Trade of Prescription Medications Through X (Formerly Twitter) in Japan: Cross-Sectional Study. JMIR Formative Research 2024;8:e54023 View

Books/Policy Documents

  1. Alasmari A, Zhou L. Social Computing and Social Media. Communication and Social Communities. View
  2. Khaja H, Abate M, Zheng W, Abbasi A, Adjeroh D. Social, Cultural, and Behavioral Modeling. View
  3. He Z. Social Web and Health Research. View