Advertisement

Medic-Us: Advanced Social Networking for Intelligent Medical Services and Diagnosis

  • Gandhi Hernández-Chan
  • Alejandro Molina VillegasEmail author
  • Mario Chirinos Colunga
  • Oscar S. Siordia
  • Alejandro Rodríguez-González
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 815)

Abstract

Health services are on the top priorities for society, but up to now we have failed in make it universal all around the world. Nowadays information technologies, especially social networks have demonstrated its usefulness in different areas. This article describes the design and development of a social network platform focused on the physician-patient and physician-physician interactions, in order to achieve better and faster diagnosis. Like other social networks or social media tools, it focusses on the collaboration among its members. This collaboration is improved with the help of paradigms as Collaborative Intelligence and Wisdom of the Crowd. We called this platform Medic-Us high-lighting the collaborative practice among the practitioners, and the interaction with patients. This document describes the different modules of Medic-Us, the social network environment, medical consult service, information retrieval, and a trainer module for the medicine students.

Keywords

Social networks Decision support system Semantic web Diagnostic system Collaborative intelligence 

References

  1. 1.
    Israel, B.A.: Social networks and social support: implications for natural helper and community level interventions. Health Educ. Q. 12(1), 65–80 (1985)CrossRefGoogle Scholar
  2. 2.
    Heylighen, F.: 2 Collective intelligence and its implementation on the web: algorithms to develop a collective mental map. Comput. Math. Org. Theory 5(3), 253–280 (1999)CrossRefGoogle Scholar
  3. 3.
    Surowiecki, J.: The wisdom of crowds. Anchor (2005)Google Scholar
  4. 4.
    Alag, S.: Collective intelligence in action. Manning Publications Co. (2008)Google Scholar
  5. 5.
    Barsky, E.: Introducing web 2.0: weblogs and podcasting for health librarians. J. Canadian Health Lib. Assoc. J. de l Assoc. des bibliotheques de la sante du Canada 27(2), 33–34 (2006)CrossRefGoogle Scholar
  6. 6.
    Rohani, V.A., Hock, O.S.: On social network web sites: definition, features, architectures and analysis tools. J. Comput. Eng. 1, 3–11 (2009)Google Scholar
  7. 7.
    Eysenbach, G.: What is e-health? J. Med. Internet Res. (2001)Google Scholar
  8. 8.
    Judd, T., Kennedy, G.: Expediency-based practice? medical students’ reliance on google and wikipedia for biomedical inquiries. Brit. J. Educ. Technol. 42(2), 351–360 (2011)CrossRefGoogle Scholar
  9. 9.
    Lavsa, S.M., Corman, S.L., Culley, C.M., Pummer, T.L.: Reliability of wikipedia as a medication information source for pharmacy students. Currents Pharm. Teach. Learn. 3(2), 154–158 (2011)CrossRefGoogle Scholar
  10. 10.
    Hernández-Chan, G.S., Ceh-Varela, E.E., Sanchez-Cervantes, J.L., Vil-lanueva-Escalante, M., Rodríguez-González, A., Pérez-Gallardo, Y.: Collective intelligence in medical diagnosis systems: a case study. Comput. Biol. Med. 74, 45–53 (2016)CrossRefGoogle Scholar
  11. 11.
    KamelBoulos, M.N., Wheeler, S.: The emerging web 2.0 social software: an enabling suite of sociable technologies in health and health care education. Health Inf. Lib. J. 24(1), 2–23 (2007)CrossRefGoogle Scholar
  12. 12.
    Giustini, D.: How web 2.0 is changing medicine. Brit. Med. J. Publ. Group (2006)Google Scholar
  13. 13.
    Sandars, J., Schroter, S.: Web 2.0 technologies for undergraduate and post-graduate medical education: an online survey. Postgrad. Med. J. 83(986), 759–762 (2007)CrossRefGoogle Scholar
  14. 14.
    Giustini, D.: Web 3.0 and medicine. Brit. Med. J. Publ. Group (2007)Google Scholar
  15. 15.
    Boulos, M.N.K., Maramba, I., Wheeler, S.: Wikis, blogs and podcasts: a new generation of web-based tools for virtual collaborative clinical practice and education. BMC Med. Educ. 6(1), 41 (2006)CrossRefGoogle Scholar
  16. 16.
    Gruber, T.: Collective knowledge systems: where the social web meets the se-mantic web. Web Semantics Sci. Serv. Agents World Wide Web 6(1), 4–13 (2008)CrossRefGoogle Scholar
  17. 17.
    Zhdanova, A.V.: Community-driven ontology construction in social networking portals. Web Intel. Agent Syst. Int. J. 6(1), 93–121 (2008)Google Scholar
  18. 18.
    Tellez, E.S., Miranda-Jiménez, S., Graff, M., Moctezuma, D., Siordia, O.S., and Villaseñor, E.A.: A case study of spanish text transformations for twitter sentiment analysis. Expert Syst. Appl. 81, 457–471 (2017).  https://doi.org/10.1016/j.eswa.2017.03.071CrossRefGoogle Scholar
  19. 19.
    Rodriguez-Gonzalez, A., Hernandez-Chan, G., Colomo-Palacios, R., Mi-guel Gomez-Berbis, J., Garcia-Crespo, A., Alor-Hernandez, G., Valencia-Garcia, R.: Towards an ontology to support semantics enabled diagnostic decision support systems. Curr. Bioinf. 7(3), 234–245 (2012)CrossRefGoogle Scholar
  20. 20.
    Spackman, K.: Snomedct: style guide: observables and investigation procedures (laboratory). Int. Health Terminol. Stand. Develop. Org. (2010)Google Scholar
  21. 21.
    Corcho, O., Fernández-Lopez, M., Gómez-Pérez, A.: “Methodologies, tools and languages for building ontologies”, where is their meeting point? Data Knowl. Eng. 46(1), 41–64 (2003)CrossRefGoogle Scholar
  22. 22.
    Pinto, H.S., Gomez-Pérez, A., Martins, J.P.: Some issues on ontology integration. In: IJCAI and the Scandinavian AI Societies. CEUR Workshop Proceedings (1999)Google Scholar
  23. 23.
    Miller, N., Lacroix, E.M., Backus, J.E.: Medlineplus: building and maintaining the national library of medicine’s consumer health web service. Bull. Med. Libr. Assoc. 88(1), 11 (2000)Google Scholar
  24. 24.
    Tsumoto, S.: Automated extraction of medical expert system rules from clinical databases based on rough set theory. Inf. Sci. 112(1–4), 67–84 (1998)CrossRefGoogle Scholar
  25. 25.
    Tan, K.C., Yu, Q., Heng, C., Lee, T.H.: Evolutionary computing for knowledge discovery in medical diagnosis. Artif. Intel. Med. 27(2), 129–154 (2003)CrossRefGoogle Scholar
  26. 26.
    Hahn, U., Romacker, M., Schulz, S.: Medsyndikate—a natural language system for the extraction of medical information from findings reports. Int. J. Med. Inf. 67(1–3), 63–74 (2002)CrossRefGoogle Scholar
  27. 27.
    Do Amaral, M.B., Roberts, A., Rector, A.L.: Nlp techniques associated with the opengalen ontology for semi-automatic textual extraction of medical knowledge: abstracting and mapping equivalent linguistic and logical constructs. In: Proceedings of the AMIA Symposium, p. 76. American Medical Informatics Association (2000)Google Scholar
  28. 28.
    Rodríguez-Gonzalez, A., Martínez-Romero, M., Costumero, R., Wil-kinson, M.D., Menasalvas-Ruiz, E.: Diagnostic knowledge extraction from med-lineplus: an application for infectious diseases. In: 9th International Conference on Practical Applications of Computational Biology and Bioinformatics, pp. 79–87. Springer (2015)Google Scholar
  29. 29.
    Elkin, P.L., Brown, S.H., Husser, C.S., Bauer, B.A., Wahner-Roedler, D., Rosenbloom, S.T., Speroff, T.: Evaluation of the content coverage of snomed-ct: ability of snomed clinical terms to represent clinical problem lists. In: Mayo Clinic Proceedings, vol. 81, pp. 741–748. Elsevier (2006)Google Scholar
  30. 30.
    McBride, B.: Jena: implementing the RDF model and syntax specification. In: Proceedings of the Second International Conference on Semantic Web, vol. 40, pp. 23–28 (2001)Google Scholar
  31. 31.
    Knight, K., Marcu, D.: Summarization beyond sentence extraction: a probabilistic approach to sentence compression. Artif. Intell. 139(1), 91–107 (2002)CrossRefGoogle Scholar
  32. 32.
    McDonald, R.: Discriminative sentence compression with soft syntactic evidence. Proc. EACL 6, 297–304 (2006)Google Scholar
  33. 33.
    Molina, A., Torres-Moreno, J.M., SanJuan, E., Da Cunha, I., Martínez, G.E.S.: Discursive sentence compression. In: International Conference on Intelligent Text Processing and Computational Linguistics, pp. 394–407. Springer (2013)Google Scholar
  34. 34.
    Sporleder, C., Lapata, M.: Discourse chunking and its application to sentence compression. In: Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 257–264. ACL (2005)Google Scholar
  35. 35.
    Molina, A.: Compresión automática de frases: un estudio hacia la gene-ración de resúmenes en español. Intel. Artif. 16(51), 41–62 (2013)Google Scholar
  36. 36.
    Chen, S., Goodman, J.: An empirical study of smoothing techniques for language modeling. Comput. Speech Lang. 13(4), 359–393 (1999)CrossRefGoogle Scholar
  37. 37.
    Stolcke, A.: Srilm—an extensible language modeling toolkit. In: International Conference on Spoken Language Processing, vol. 2, pp. 901–904. Denver (2002)Google Scholar
  38. 38.
    Tanabe, L., Xie, N., Thom, L.H., Matten, W., Wilbur, W.J.: Genetag: a tagged corpus for gene/protein named entity recognition. BMC Bioinf. 6(1), 1 (2005)CrossRefGoogle Scholar
  39. 39.
    Rockt¨aschel, T., Weidlich, M., Leser, U.: Chemspot: a hybrid system for chemical named entity recognition. Bioinformatics 28(12), 1633–1640 (2012)CrossRefGoogle Scholar
  40. 40.
    Sobhana, N., Mitra, P., Ghosh, S.: Conditional random field based named entity recognition in geological text. Int. J. Comput. Appl. 1(3), 143–147 (2010)Google Scholar
  41. 41.
    Smith, D.A., Crane, G.: Disambiguating geographic names in a historical digital library. In: Research and Advanced Technology for Digital Libraries, pp. 127–136. Springer (2001)Google Scholar
  42. 42.
    Hirschman, L., Yeh, A., Blaschke, C., Valencia, A.: Overview of biocreative: critical assessment of information extraction for biology. BMC Bioinf. 6(Suppl 1), S1 (2005)CrossRefGoogle Scholar
  43. 43.
    Hernandez-Chan, G.S., Ceh-Varela, E.E., Cervera-Evia, G., Quijano-Aban, V.: Using semantic technologies for an intelligent medical trainer. In: International Symposium on Intelligent Computing Systems, pp. 74–82. Springer (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Gandhi Hernández-Chan
    • 1
  • Alejandro Molina Villegas
    • 1
    Email author
  • Mario Chirinos Colunga
    • 1
  • Oscar S. Siordia
    • 1
  • Alejandro Rodríguez-González
    • 2
  1. 1.CONACYT – Centro de Investigación en Ciencias de la Información GeoespacialMexico CityMexico
  2. 2.Centro de Tecnología Biomédica - Universidad Politécnica de MadridMadridSpain

Personalised recommendations