  The Internet has revolutionized healthcare by offering medical information
ubiquitously to patients via web search. The healthcare status, complex medical
information needs of patients are expressed diversely and implicitly in their
medical text queries. Aiming to better capture a focused picture of user's
medical-related information search and shed insights on their healthcare
information access strategies, it is challenging yet rewarding to detect
structured user intentions from their diversely expressed medical text queries.
We introduce a graph-based formulation to explore structured concept
transitions for effective user intent detection in medical queries, where each
node represents a medical concept mention and each directed edge indicates a
medical concept transition. A deep model based on multi-task learning is
introduced to extract structured semantic transitions from user queries, where
the model extracts word-level medical concept mentions as well as
sentence-level concept transitions collectively. A customized graph-based
mutual transfer loss function is designed to impose explicit constraints and
further exploit the contribution of mentioning a medical concept word to the
implication of a semantic transition. We observe an 8% relative improvement in
AUC and 23% relative reduction in coverage error by comparing the proposed
model with the best baseline model for the concept transition inference task on
real-world medical text queries.
