  The increasing reliance on online communities for healthcare information by
patients and caregivers has led to the increase in the spread of
misinformation, or subjective, anecdotal and inaccurate or non-specific
recommendations, which, if acted on, could cause serious harm to the patients.
Hence, there is an urgent need to connect users with accurate and tailored
health information in a timely manner to prevent such harm. This paper proposes
an innovative approach to suggesting reliable information to participants in
online communities as they move through different stages in their disease or
treatment. We hypothesize that patients with similar histories of disease
progression or course of treatment would have similar information needs at
comparable stages. Specifically, we pose the problem of predicting topic tags
or keywords that describe the future information needs of users based on their
profiles, traces of their online interactions within the community (past posts,
replies) and the profiles and traces of online interactions of other users with
similar profiles and similar traces of past interaction with the target users.
The result is a variant of the collaborative information filtering or
recommendation system tailored to the needs of users of online health
communities. We report results of our experiments on an expert curated data set
which demonstrate the superiority of the proposed approach over the state of
the art baselines with respect to accurate and timely prediction of topic tags
(and hence information sources of interest).
