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We propose a new task for FIRE 2016 on Consumer Health Information Search (CHIS) for FIRE 2016.
World Wide Web is increasingly being used by consumers as an aid for health decision making and for self-management of chronic illnesses as evidenced by the fact that one in every 20 searches on google is about health. Information access mechanisms for factual health information retrieval have matured considerably, with search engines providing Fact checked Health Knowledge Graph search results to factual health queries. It is pretty straightforward to get an answer to the query “what are the symptoms of Diabetes” from the search engines.
However retrieval of relevant multiple perspectives for complex health search queries which do not have a single definitive answer still remains elusive with most of the general purpose search engines. For example, a user health query such as “can metabolic therapy cure brain cancer” causes considerable frustration for the searcher as he needs to wade through hundreds of search results to obtain a balanced view of the diverse perspectives/points of view available, both for and against the hypothesis posed in the search query. The presence of multiple perspectives with different grades of supporting evidence (which is dynamically changing over time due to the arrival of new research and practice evidence) makes it all the more challenging for a lay searcher.
We use the term “Consumer Health Information Search” (CHIS) to denote such information retrieval search tasks, for which there is “No Single Best Correct Answer”; Instead multiple and diverse perspectives/points of view (which very often are contradictory in nature) are available on the web regarding the queried information. The proposed CHIS track investigates complex health information search in scenarios where users search for health information with more than just a single correct answer, and look for multiple perspectives from diverse sources both from medical research and from real world patient narratives. The goal of CHIS lab track is to research and develop techniques to support users in complex multi-perspective health information queries.
Given a CHIS query, and a document/set of documents associated with that query, the task is to classify the sentences in the document as relevant to the query or not. The relevant sentences are those from that document, which are useful in providing the answer to the query. These relevant sentences need to be further classified as supporting the claim made in the query, or opposing the claim made in the query.
Example query: Does daily aspirin therapy prevent heart attack?
S1: “Many medical experts recommend daily aspirin therapy for preventing heart attacks in people of age fifty and above.” [affirmative/Support]
S2: “While aspirin has some role in preventing blood clots, daily aspirin therapy is not for everyone as a primary heart attack prevention method”. [disagreement/Oppose]