title: Health consumer-oriented information retrieval.
abstract: While patients can freely access their Electronic Health Records or online health information, they may not be able to correctly understand the content of these documents. One of the challenges is related to the difference between expert and non-expert languages. We propose to investigate this issue within the Information Retrieval field. The patient queries have to be associated with the corresponding expert documents, that provide trustworthy information. Our approach relies on a state-of-the-art IR system called Indri and on semantic resources. Different query expansion strategies are explored. Our system shows up to 0.6740 P@10, up to 0.7610 R@10, and up to 0.6793 NDCG@10.
authors.affiliation: IRISA - CNRS, Rennes, France.
authors.forename: Vincent
authors.initials: V
authors.lastname: Claveau
authors.affiliation: LIMSI - CNRS, Orsay, France; Université Paris 13, Sorbonne Paris Cité, France.
authors.forename: Thierry
authors.initials: T
authors.lastname: Hamon
authors.affiliation: IRISA - CNRS, Rennes, France.
authors.forename: Sébastien
authors.initials: S
authors.lastname: Le Maguer
authors.affiliation: STL UMR8163 CNRS, Université Lille 3, France.
authors.forename: Natalia
authors.initials: N
authors.lastname: Grabar
mesh_terms: D054626:Consumer Health Information / Q000458:organization & administration*; D057225:Data Mining / Q000379:methods*; D057286:Electronic Health Records / Q000458:organization & administration*; D063005:Health Information Systems / Q000458:organization & administration*; D000069550:Machine Learning; D009323:Natural Language Processing; D033283:Patient Access to Records; D014584:User-Computer Interface