Bringing Semantic Structures to User Intent Detection in Online Medical Queries

TL;DR

This paper presents a graph-based deep learning model for detecting user intent in online medical queries, capturing structured semantic transitions to improve understanding of patient information needs.

Contribution

It introduces a novel graph-based formulation and multi-task deep learning model with a custom loss for structured intent detection in medical queries, outperforming baseline methods.

Findings

8% relative improvement in AUC

23% relative reduction in coverage error

Effective modeling of medical concept transitions

Abstract

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.