Test collections for medical information retrieval evaluation

The web has rapidly become one of the main resources for medical information for many people: patients, clinicians, medical doctors, etc. Measuring the effectiveness with which information can be retrieved from web resources for these users is crucial: it brings better information to professionals for better diagnosis, treatment, patient care; and helps patients and relatives get informed on their condition. Several existing information retrieval (IR) evaluation campaigns have been developed to assess and improve medical IR methods, for example the TREC Medical Record Track [11] and TREC Genomics Track [10]. These campaigns only target certain type of users, mainly clinicians and some medical professionals: queries are mainly centered on cohorts of records describing a specific patient cases or on biomedical reports. Evaluating search effectiveness over the many heterogeneous online medical information sources now available, which are increasingly used by a diverse range of medical professionals and, very importantly, the general public, is vital to the understanding and development of medical IR. We describe the development of two benchmarks for medical IR evaluation from the Khresmoi project. The first of these has been developed using existing medical query logs for internal research within the Khresmoi project and targets both medical professionals and general public; the second has been created in the framework of a new CLEFeHealth evaluation campaign and is designed to evaluate patient search in context

Unsupervised and semi-supervised morphological analysis for Information Retrieval in the biomedical domain

International audienceIn the biomedical field, the key to access information is the use of specialized terms. However, in most of Indo-European languages, these terms are complex morphological structures. The aim of the presented work is to identify the various meaningful components of these terms and use this analysis to improve biomedical Information Retrieval. We present an approach combining an automatic alignment using a pivot language, and an analogical learning that allows an accurate morphological analysis of terms. These morphological analysis are used to improve the indexing of medical documents. The experiments reported in this paper show the validity of this approach with a 10% improvement in MAP over a standard IR system