Tell me what i don't know - Making the most of social health forums

Jerry Rolia, Wen Yao, Sujoy Basu, Wei Nchih Lee, Sharad Singhal, Akhil Kumar, Sharat R. Sabbella

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


We describe a novel approach to helping patients learn about their condition using social health forums. We infer a patient's condition based on her clinical data, and assign her to a clinical state pertaining to that condition. Our system presents the patient with forum posts that are most relevant to her current state. By adjusting a Like Me dial, the patient can modify the results from those that are most popular with patients in the same condition to the most general that apply to patients that map to other states for the condition. The posts are ranked in order of importance to the patient condition and the dial settings and the Top-N results are shown to the patient. The initial rankings of posts for different states are obtained by weights assigned by a medical expert. The patients can also give feedback with a LIKE or a DISLIKE button on whether the post is useful to them. This is incorporated into our method to dynamically change the initial weights. As the system is used in practice the initial rankings are subsumed by the feedback provided by users of the system. Our approach is general but we give results in the context of a diabetes social health forum. The algorithms are given along with preliminary results and an illustration of the user interface. We view this effort as a component of a larger patient navigation and engagement system that can help patients gain a better understanding of their condition.

Original languageEnglish (US)
JournalHP Laboratories Technical Report
Issue number43
StatePublished - 2013

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications


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