Skip to main content
SpringerLink
Log in

Medical social networks content mining for a semantic annotation

  • Original Article
  • Published:
Social Network Analysis and Mining Aims and scope Submit manuscript

Abstract

The interactions between subscribers of the health-related social networking (HSNs) platforms rise the production and sharing of a huge amount of multimedia content, daily, by permitting them to upload their medical images. These images become the centre of communication in various multilingual expressions immediately describing observations, comments and health checkups. As a part of this exchange, it is clear that these spaces are a valuable source of subscribers-generated information. Besides, it is still an open question to enable subscribers to investigate relevant information, due to the diversity of the available content. So, it is vital to engage new mechanisms in order to pull out information and acquaintance from this content. For this purpose, we have implemented a content analysis model of health-related information to get an overview of the medical content available. We present a semantic terms-based approach to pull out pertinent terms and concepts from the text material. As a result, notable extracted terms and keywords will be applied, subsequently, to present to annotate medical images, to direct users to an appropriate seeking task, through the SN site. So, the analysis method concentrates on algorithms based on statistical methods and external multilingual semantic resources to cover and treat this situation. It is essential also to deal with such ambiguities causing the efficacy decreasing of the search function. Our study is validated by a set of experiments and compared with some existing models. Experimental results have ensured that the presented model has better findings, in terms of performance and satisfaction.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Notes

  1. https://metamap.nlm.nih.gov/.

  2. http://ai.stanford.edu/~rion/parsing/minipar_viz.html.

  3. http://www.cis.uni-muenchen.de/~schmid/tools/TreeTagger/.

  4. http://www.lexilogos.com/medical_dictionnaire.htm.

  5. http://www.nzdl.org/Kea/download.html.

  6. http://perso.univ-lyon2.fr/~maniezf/Corpus/Corpus_medical_FR_CRTT.htm.

References

  • Alexander S, McLawhorn I, De Martino KA, Fehring P, Sculco K (2016) Social media and your practice: navigating the surgeon-patient relationship. Curr Rev Musculoskelet Med 9:487–495. https://doi.org/10.1007/s12178-016-9376-1

    Article  Google Scholar 

  • Bodenreider O (2004) The unified medical language system (UMLS): integrating biomedical terminology. Nucl Acids Res 32:267–270

    Article  Google Scholar 

  • Bouslimi R, Akaichi J (2015) Automatic medical image annotation on social network of physician collaboration. Netw Model Anal Health Inform Bioinforma 4:10. https://doi.org/10.1007/s13721-015-0082-5

    Article  Google Scholar 

  • Choi S, Choi J (2014) Exploring effective information retrieval technique for the medical web documents: SNUMedinfo at CLEFeHealth2014 task 3. In: Proceedings of the ShARe/CLEF eHealth evaluation lab

  • Glover M, Choy G, Boland GW et al (2015) Radiology and social media: are private practice radiology groups more social than academic radiology departments? J Am College Radiol 12(5):513–518. https://doi.org/10.1016/j.jacr.2014.11.005

    Article  Google Scholar 

  • Goeuriot L, Jones GJF, Kelly L, Müller H, Zobel J (2016) Medical information retrieval: introduction to the special issue. Inf Retrieval J 19:1–5. https://doi.org/10.1007/s10791-015-9277-8

    Article  Google Scholar 

  • Harrathi F (2010) Extraction de concepts et de relations entre concepts à partir des documents multilingues: approche statistique et ontologique. Ph.D. Thesis, INSA Lyon

  • Hernandeza EM, Erin P, Jonathan B (2019) Social networks and the emergence of health inequalities following a medical advance: examining prenatal H1N1 vaccination decisions. Soc Netw 58:156–167. https://doi.org/10.1016/j.socnet.2019.03.002

    Article  Google Scholar 

  • Hinojo-Lucena FJ, Inmaculada AD, María-Pilar CR, José-María RR (2018) Use of social networks for international collaboration among medical students. Educación Médica 21(2):137–141. https://doi.org/10.1016/j.edumed.2018.08.009

    Article  Google Scholar 

  • Jaccard P (1901) Distribution de la flore alpine dans le Bassin des Drouces et dans quelques regions voisines. Bull Soc Vaud Sci Nat 37(140):241–272

    Google Scholar 

  • Jones K (1972) A statistical interpretation of term specificity and its application in retrieval. J Doc 28(1):11–21

    Article  Google Scholar 

  • Koopman B, Zuccon G, Bruza P, Sitbon L, Lawley M (2016) Information retrieval as semantic inference: a graph inference model applied to medical search. Informa Retrieval J 19(1–2):6–37. https://doi.org/10.1007/s10791-015-9268-9

    Article  Google Scholar 

  • Kordzadeh N (2016) Social media in health care. Contemporary consumer health informatics. Springer, Berlin, pp 101–23. https://doi.org/10.1007/978-3-319-25973-4_6

  • Kovacs W, Weisenthal S, Folio L, Li Q, Summers RM, Yao J (2016) Retrieval, visualization, and mining of large radiation dosage data. Inform Retrieval J 19(1–2):38–58. https://doi.org/10.1007/s10791-015-9265-z

    Article  Google Scholar 

  • Ksentini N, Tmar M, Gargour F (2014) Miracl at CLEF 2014: eHealth information retrieval task. In: Proceedings of the ShARe/CLEF eHealth evaluation lab

  • Levenshtein VI (1966) Binary codes capable of correcting deletions, insertions and reversals. Sov Phys Dokl 10:707–710

    MathSciNet  Google Scholar 

  • Lossio-Ventura JA, Jonquet C, Roche M, Teisseire M (2016) Biomedical term extraction: overview and a new methodology. Inform Retrieval J 19(1–2):59–99. https://doi.org/10.1007/s10791-015-9262-2

    Article  Google Scholar 

  • Maisonnasse L, Gaussier E, Chevallet J-P (2009) Combination of semantic analysis to search for medical information. In: RISE (Research Information semantics) within the INFORSID' conference, Toulouse

  • Manning CD, Raghavan P, Schütze H (2008) Introduction to information retrieval. Cambridge University Press, New York

    Book  MATH  Google Scholar 

  • Mao J, Lu K, Mu X, Li G (2015) Mining document, concept, and term associations for effective biomedical retrieval: introducing MeSH enhanced retrieval models. Inform Retrieval J 18:413–444

    Article  Google Scholar 

  • Markonis D, Schaer R, Muller H (2016) Evaluating multimodal relevance feedback techniques for medical image retrieval. Inform Retrieval J 19(1–2):100–112. https://doi.org/10.1007/s10791-015-9260-4

    Article  Google Scholar 

  • Martinez D, Otegi A, Soroa A, Agirre E (2014) Improving search over Electronic Health Records using UMLS-based query expansion through random walks. J Biomed Inform 51:100–106

    Article  Google Scholar 

  • McGowan BS, Wasko M, Vartabedian BS, Miller RS, Freiherr DD, Abdolrasulnia M (2012) Understanding the factors that influence the adoption and meaningful use of social media by physicians to share medical information. J Med Internet Res 14(5):e117. https://doi.org/10.2196/jmir.2138

    Article  Google Scholar 

  • NLM (2009) NLM Unified Medical Language System Fact Sheet. Available from: http://www.nlm.nih.gov/pubs/factsheets/umls.html.

  • Oh HS, Jung Y (2014) A multiple-stage approach to re-ranking clinical documents. In: Proceedings of the ShARe/CLEF eHealth evaluation lab

  • Ozturkmenogl O, Alpkocak A, Kilinc D (2014) Demir at CLEF eHealth the effects of selective query expansion to information retrieval. In: Proceedings of the ShARe/CLEF eHealth evaluation lab

  • Perrin A (2015) Social networking usage: 2005–2015. Pew Research Center. Available at: http://www.pewinternet.org/2015/10/08/2015/Social-Networking-Usage-2005-2015/. Accessed 3 Jan 2018

  • Ratib O (2014) Social media networks in communication between radiologists, non-radiologists and patients ECR 2014, Part A. Insights Imaging 5:69. https://doi.org/10.1007/s13244-014-0316-6

    Article  Google Scholar 

  • Roberts K, Simpson M, Demner-Fushman D, Voorhees E, Hersh W (2016) State-of-the-art in biomedical literature retrieval for clinical cases: a survey of the TREC 2014 CDS track. Inform Retrieval J 19(1–2):113–148. https://doi.org/10.1007/s10791-015-9259-x

    Article  Google Scholar 

  • Salton G (2015) The SMART retrieval system—experiments in automatic document processing. Prentice-Hall Inc, Upper Saddle River

    Google Scholar 

  • Şenol D, Özgür U, Murat K, Cuma Ç (2021) Socio-demographic characteristics affect health information seeking on the Internet in Turkey. Health Inform Librar J. https://doi.org/10.1111/hir.12358

    Article  Google Scholar 

  • Sfakianaki P, Koumakis L, Sfakianakis S, Iatraki G, Zacharioudakis G, Graf N et al (2015) Semantic biomedical resource discovery: a natural language processing framework. BMC Med Inform Decision Making 15:77

    Article  Google Scholar 

  • Shen W, Nie JY, Liu X, Liui X (2014) An investigation of the effectiveness of concept-based approach in medical information retrieval GRIUM @ CLEF2014eHealth task 3. In: Proceedings of the ShARe/CLEF eHealth evaluation lab

  • Soldaini L, Yates A, Yom-Tov E, Frieder O, Goharian N (2016) Enhancing web search in the medical domain via query clarification. Inform Retrieval J 19(1–2):149–173. https://doi.org/10.1007/s10791-015-9258-y

    Article  Google Scholar 

  • Suominen H, et al. (2012) The proceedings of the CLEFeHealth2012—the CLEF 2012 workshop on cross-language evaluation of methods, applications, and resources for eHealth Document Analysis. NICTA

  • Suryavanshi T, Geier CD, Leland JM, Silverman L, Duggal N (2016) Social media and orthopaedics: opportunities and challenges. Instr Course Lect 65:645–653

    Google Scholar 

  • Thesprasith O, Jaruskulchai C (2014) Csku gprf-qe for medical topic web retrieval. In: Proceedings of the ShARe/CLEF eHealth evaluation lab

  • Yanshan Wang S, Liu N, Afzal MR, Liwei W, Feichen S, Paul K, Hongfang L (2018) A comparison of word embeddings for the biomedical natural language processing. J Biomed Inform 87:12–20. https://doi.org/10.1016/j.jbi.2018.09.008

    Article  Google Scholar 

  • Yip HY, Taib NA, Khan HA, Dhillon SK (2019) Electronic health record integration. Encycl Bioinform Comput Biol 2:1063–1076. https://doi.org/10.1016/B978-0-12-809633-8.20306-3

    Article  Google Scholar 

  • Zember WF, Fishman EK, Horton KM (2015) How social media can impact medicine and radiology. J Am College Radiol 12(6):620–621. https://doi.org/10.1016/j.jacr.2014.07.030

    Article  Google Scholar 

  • Zong X, Xiangqian D, Zhen Z (2021) Health level classification by fusing medical evaluation from multiple social networks. Future Gener Comput Syst 114:574–580. https://doi.org/10.1016/j.future.2020.08.008

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Sciences, ISG, BESTMOD, Tunis, Tunisia

    Mouhamed Gaith Ayadi, Riadh Bouslimi & Jalel Akaichi

Authors
  1. Mouhamed Gaith Ayadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Riadh Bouslimi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jalel Akaichi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mouhamed Gaith Ayadi.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ayadi, M.G., Bouslimi, R. & Akaichi, J. Medical social networks content mining for a semantic annotation. Soc. Netw. Anal. Min. 12, 17 (2022). https://doi.org/10.1007/s13278-021-00848-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13278-021-00848-7

Keywords

Search

Navigation