Towards a medical question-answering system: a feasibility study.

Question-answering (QA) systems, as have been presented and evaluated in several TREC conferences, are the next generation of search engines. They combine 'traditional' Information Retrieval (IR) with Natural Language Processing (NLP) and Knowledge Engineering techniques to provide shorter, more precise answers to natural language questions. We study here the feasibility of such a system for French in the health care domain. In this purpose, we collected a corpus of student questions in oral surgery. We examined two enabling conditions: on the IR side, how to select the right keywords in a question to identify relevant material on the Web for answering this question, a prerequisite for success; and on the NLP side, whether the contents of the questions fit the conceptual model of an existing QA prototype, a favorable condition for rapid implementation. A manual Web search enabled us to devise automatable principles for building IR queries for these questions. Besides, we could design a semantic model, using UMLS Semantic Network relations, which is consistent with our prototype and covers 90% of the questions. However, the high specialization of the domain and the clinical orientation of the questions, joined with the more limited resources online in the French language, may restrain the quantity of Web material available for answering these questions.