Augmented Therapy with Online Support Groups

Support groups are often formed in hospitals and clinics to enable group therapy. A support group consists of patients suffering from a same disease. Manual formation of support groups has three drawbacks. First, it is “local”, i.e., a support group for a specific type of cancer in a local hospital may contain patients with different symptoms and treatments. Discussions in such heterogeneous groups are not necessarily useful for their members. Second, support groups are often “static” and do not meet emerging needs of patients. Third, there may not be enough motivation in patients to join such groups. In this paper, we use the social Web to envision a framework for the automatic formation of dynamic support groups. Our framework consists of several components to build support groups, motivate patients to join, and keep them engaged in those groups.

[1]  George A. Miller,et al.  Human memory and the storage of information , 1956, IRE Trans. Inf. Theory.

[2]  P. Schwartz,et al.  Psychosocial benefits of a cancer support group , 1986, Cancer.

[3]  P. Klemm,et al.  A nontraditional cancer support group. The Internet. , 1998, Computers in nursing.

[4]  Jaideep Srivastava,et al.  Automatic personalization based on Web usage mining , 2000, CACM.

[5]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[6]  M. T. Høybye,et al.  Online interaction. Effects of storytelling in an internet breast cancer support group , 2005 .

[7]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[8]  P. Butow,et al.  What do cancer support groups provide which other supportive relationships do not? The experience of peer support groups for people with cancer. , 2006, Social science & medicine.

[9]  Yehuda Koren,et al.  Collaborative filtering with temporal dynamics , 2009, KDD.

[10]  P. Pfeiffer,et al.  Why are some patients in treatment for advanced cancer reluctant to consult their GP? , 2009, Scandinavian journal of primary health care.

[11]  G. Maccauro,et al.  Psychological Stress and Cancer , 2011, International journal of immunopathology and pharmacology.

[12]  Idan Szpektor,et al.  I want to answer; who has a question?: Yahoo! answers recommender system , 2011, KDD.

[13]  M. Swan.  Crowdsourced Health Research Studies: An Important Emerging Complement to Clinical Trials in the Public Health Research Ecosystem , 2012, Journal of medical Internet research.

[14]  James B. Weaver,et al.  Social Media Engagement and Public Health Communication: Implications for Public Health Organizations Being Truly “Social” , 2013, Public Health Reviews.

[15]  O. Yersal,et al.  Biological subtypes of breast cancer: Prognostic and therapeutic implications. , 2014, World journal of clinical oncology.

[16]  Martin Schader,et al.  Personalized task recommendation in crowdsourcing information systems - Current state of the art , 2014, Decis. Support Syst..

[17]  Alexandre Termier,et al.  Interactive User Group Analysis , 2015, CIKM.

[18]  Sihem Amer-Yahia,et al.  Multi-Objective Group Discovery on the Social Web , 2016, ECML/PKDD.

[19]  Hao Ma,et al.  Table Cell Search for Question Answering , 2016, WWW.

[20]  Avi Parush,et al.  Human Factors in Healthcare: A Field Guide to Continuous Improvement , 2017, Synthesis Lectures on Assistive, Rehabilitative, and Health-Preserving Technologies.

[21]  Michael S. Bernstein,et al.  In Search of the Dream Team: Temporally Constrained Multi-Armed Bandits for Identifying Effective Team Structures , 2018, CHI.

[22]  Laks V. S. Lakshmanan,et al.  Cohort Representation and Exploration , 2018, 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA).