Bringing Semantic Structures to User Intent Detection in Online Medical Queries
This work addresses the problem of improving healthcare information access for patients by enhancing intent detection in online medical queries, though it is incremental as it builds on existing methods with specific gains.
The paper tackled the challenge of detecting structured user intentions from diversely expressed medical text queries by introducing a graph-based formulation and a multi-task deep model, achieving an 8% relative improvement in AUC and a 23% relative reduction in coverage error compared to the best baseline.
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.