Social Networks and Automated Mental Health Screening

Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 491)

Abstract

A health social network is an online information service which facilitates information sharing between closely related members of a community. The main means of finding patients with similar health conditions has been based on labor-intensive methods, such as searching the Internet. Also, because of the privacy and security issues of health information systems, it is often difficult to find patients who can support each other in the community. Over the years, many automated recommender systems have been developed for social networking. We propose a social networking framework for patient care, where health service providers facilitate social links between parents using similarities of mental health conditions. A machine learning approach was developed to automatically generate keywords for mental health descriptions that can be used to screen for mental health conditions and to then group individuals with similar mental health conditions. Keywords are generated from sources such as conversations on online forums. These keywords are then used to identify similarities between mental health descriptions, in order to recommend a community of related patients.

Notes

Acknowledgments

We would like to thank Yi Pin Song for collecting text documents from various Internet health forums. This work was supported by JCU Research Grant JCUS/003/2011/IS, a grant from the Bill & Melinda Gates Foundation through the Grand Challenges Explorations Initiative (Grant Number: OPP1032125), and T-BOP PTE LTD (http://www.t-bop.com).

References

  1. 1.
    Elkin, N.: How America Searches: Health and Wellness (2008)Google Scholar
  2. 2.
    Sarasohn-Kahn, J.: The Wisdom of Patients: Health Care Meets Online Social Media. California HealthCare Foundation iHeath Reports (2008)Google Scholar
  3. 3.
    Swan, M.: Emerging patient-driven health care models: an examination of health social networks, consumer personalized medicine and quantified self-tracking. Int. J. Environ. Res. Public Health 6(2), 492–525 (2009)CrossRefGoogle Scholar
  4. 4.
    Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. Addison-Wesley, England (1999). doi:citeulike-article-id:6580677Google Scholar
  5. 5.
    Deerwester, S.C., Dumais, S.T., Landauer, T.K., Furnas, G.W., Harshman, R.A.: Indexing by latent semantic analysis. J. Am. Soc. Inf. Sci. 41, 391–407 (1990)CrossRefGoogle Scholar
  6. 6.
    Sarasohn-Kahn, J.: The Wisdom of Patients: Health Care Meets Online Social Media. California HealthCare Foundation iHeath Reports (April 2008)Google Scholar
  7. 7.
    Spertus, E., Sahami, M., Buyukkokten, O.: Evaluating similarity measures: a large-scale study in the Orkut social network. In: Grossman, R.L., Bayardo, R., Bennett, K., Vaidya, J. (eds.) Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, IL, 2005. KDD-2005: 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 678–684 (2005)Google Scholar
  8. 8.
    Deshpande, M., Karypis, G.: Item-based top-N recommendation algorithms. ACM Trans. Inf. Syst. 22(1), 143–177 (2004)CrossRefGoogle Scholar
  9. 9.
    Speer, R., Havasi, C., Lieberman, H.: AnalogySpace: reducing the dimensionality of common sense knowledge. In: AAAI’08: Proceedings of the 23rd National Conference on Artificial Intelligence, Chicago, Illinois, 2008, pp. 548–553. AAAI Press (2008). doi:citeulike-article-id:6873905Google Scholar
  10. 10.
    Singh, P., Lin, T., Mueller, E., Lim, G., Perkins, T., Zhu, W.: Open mind common sense: Knowledge acquisition from the general public (2002). doi:citeulike-article-id:311042Google Scholar
  11. 11.
    Turk, M., Pentland, A.: Eigenfaces for recognition. Cogn. Neurosci. 3, 71–86 (1991)CrossRefGoogle Scholar
  12. 12.
    Song, I., Dillon, D., Goh, T.J., Sung, M.: A health social network recommender system. In: Agents in Principle, Agents in Practice—14th International Conference, PRIMA 2011. Lecture Notes in Computer Science, pp 361-372. Springer (2011)Google Scholar
  13. 13.
    Song, I., Marsh, N.V.: Anonymous indexing of health conditions for a similarity measure. IEEE Trans. Inf. Technol. Biomed. 16(4), 737–744 (2012)CrossRefGoogle Scholar
  14. 14.
    DSM-IV: Diagnostic and Statistical Manual of Mental Disorders. American Psychiatric Association, Washington (1994)Google Scholar
  15. 15.
    ICD10: International Statistical Classification of Disease and Related Health. World Health Organization, Geneva (1992)Google Scholar
  16. 16.
    Klin, A., Lang, J., Cicchetti, D.V., Volkmar, F.R.: Brief report: Interrater reliability of clinical diagnosis and DSM-IV criteria for autistic disorder: results of the DSM-IV autism field trial. J. Autism Dev. Disord. 30(2), 163–167 (2000)CrossRefGoogle Scholar
  17. 17.
    Cortes, C., Vladimir, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)MATHGoogle Scholar
  18. 18.
    Liu, H., Push, S.: Conceptnet: A practical commonsense reasoning toolkit. BT Technol. J. 22, 211–226 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.School of Business and ITJames Cook University Australia, Singapore CampusSingaporeSingapore

Personalised recommendations