Social Networks and Automated Mental Health Screening
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, suc
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Social Networks and Automated Mental Health Screening Insu Song and John Vong
6.1 Introduction According to a survey done in 2008, most Americans (up to 80 %) rely on the Internet to find health information they use to make their health care decisions [1]. Indeed, in 2008, the Internet rivaled physicians as a source of health information [2]. Indeed, this heightened reliance on the Internet manifests itself in patients increasingly turning to the Internet for emotional support and to acquire clinical knowledge for self-care. The massive production of social media enabled by Web 2.0 offers patients a wealth of clinical knowledge, thereby providing an efficient platform for patients to support each other. The platform, also known as Health Social Network or Health 2.0, has fuelled great interest and shown massive potential to empower patients’ self-care. Some prominent examples include PatientsLikeMe1 and the IBM Patient Empowerment System.2 These newly emerged patient-driven health care services are outreach efforts to harmonize the plethora of existing frameworks and new ideas. They aim to serve as a reliable communication channel for information exchange and better collaboration among patients and doctors. The services provided by health social networks include: (a) emotional support and information sharing, (b) physician Q & As, and (c) self-tracking of conditions, their symptoms, treatment options, and other biological information [3].
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http://www.patientslikeme.com/all/patients http://www-03.ibm.com/press/us/en/pressrelease/33944.wss
I. Song (&) J. Vong School of Business and IT, James Cook University Australia, Singapore Campus, Singapore 574421, Singapore e-mail: [email protected] J. Vong e-mail: [email protected]
M. Lech et al. (eds.), Mental Health Informatics, Studies in Computational Intelligence 491, DOI: 10.1007/978-3-642-38550-6_6, Springer-Verlag Berlin Heidelberg 2014
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I. Song and J. Vong
Advances in Internet technology and the proliferation of social media, such as blogs and social networking sites, are overwhelming to the point that it is difficult to keep abreast of current affairs and growing information. With the vast amount of information on the internet, conventional methods of searching via keywords or descriptors have become laborious and mundane. For new patients, medical assessments or discussions are often overloaded with jargon, making them difficult to explore and find relevant communities. In particular, mental health descriptions and medical assessment reports contain sensitive information, and, therefore, most existing document similarity measures, such as the popular cosine-based similarity measure [4] and latent semantic analysis (LSA) [5] are not suitable, since those methods either require the entire source medical report or a large number of features that are confidential and can possibly give away patients’ sensitive information. We have developed a method of generating a small number of relevant keywords or codes that can distinguish patients base
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