Skip to main content
SpringerLink
Log in

Social Networks and Automated Mental Health Screening

  • Chapter
  • First Online:
Mental Health Informatics

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

  • 1715 Accesses

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.

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

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://www.patientslikeme.com/all/patients

  2. 2.

    http://www-03.ibm.com/press/us/en/pressrelease/33944.wss

  3. 3.

    Used ConceptNet v2.1 from the Common Sense Computing Initiative at the MIT Media Lab (http://csc.media.mit.edu).

References

  1. Elkin, N.: How America Searches: Health and Wellness (2008)

    Google Scholar 

  2. Sarasohn-Kahn, J.: The Wisdom of Patients: Health Care Meets Online Social Media. California HealthCare Foundation iHeath Reports (2008)

    Google Scholar 

  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)

    Article  Google Scholar 

  4. Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. Addison-Wesley, England (1999). doi:citeulike-article-id:6580677

    Google Scholar 

  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)

    Article  Google Scholar 

  6. Sarasohn-Kahn, J.: The Wisdom of Patients: Health Care Meets Online Social Media. California HealthCare Foundation iHeath Reports (April 2008)

    Google Scholar 

  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. Deshpande, M., Karypis, G.: Item-based top-N recommendation algorithms. ACM Trans. Inf. Syst. 22(1), 143–177 (2004)

    Article  Google Scholar 

  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:6873905

    Google Scholar 

  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:311042

    Google Scholar 

  11. Turk, M., Pentland, A.: Eigenfaces for recognition. Cogn. Neurosci. 3, 71–86 (1991)

    Article  Google Scholar 

  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. Song, I., Marsh, N.V.: Anonymous indexing of health conditions for a similarity measure. IEEE Trans. Inf. Technol. Biomed. 16(4), 737–744 (2012)

    Article  Google Scholar 

  14. DSM-IV: Diagnostic and Statistical Manual of Mental Disorders. American Psychiatric Association, Washington (1994)

    Google Scholar 

  15. ICD10: International Statistical Classification of Disease and Related Health. World Health Organization, Geneva (1992)

    Google Scholar 

  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)

    Article  Google Scholar 

  17. Cortes, C., Vladimir, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)

    MATH  Google Scholar 

  18. Liu, H., Push, S.: Conceptnet: A practical commonsense reasoning toolkit. BT Technol. J. 22, 211–226 (2004)

    Article  Google Scholar 

Download references

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).

Author information

Authors and Affiliations

  1. School of Business and IT, James Cook University Australia, Singapore Campus, Singapore, 574421, Singapore

    Insu Song & John Vong

Authors
  1. Insu Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. John Vong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Insu Song .

Editor information

Editors and Affiliations

  1. Computer Engineering, RMIT University School of Electrical and, Melbourne, Victoria, Australia

    Margaret Lech

  2. Singapore Campus, James Cook University Australia, School of Business and IT, Singapore, Singapore

    Insu Song

  3. Department of Psychiatry and Behavioral Sciences, University of California, Davis, Sacramento, California, USA

    Peter Yellowlees

  4. Psychology Network Pty Ltd, Brisbane, Australia

    Joachim Diederich

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Song, I., Vong, J. (2014). Social Networks and Automated Mental Health Screening. In: Lech, M., Song, I., Yellowlees, P., Diederich, J. (eds) Mental Health Informatics. Studies in Computational Intelligence, vol 491. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38550-6_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-38550-6_6

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38549-0

  • Online ISBN: 978-3-642-38550-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation