LIMSI @ 2014 Clinical Decision Support Track
Abstract
In this paper we present our participation in the 2014 TREC Clinical Decision Support Track. The goal of this track is to find relevant medical literature for a case report which should help address one specific clinical aspect of the case. Since it was the first time we participated in this task, we opted for an exploratory approach to test the impact of retrieval systems based on Bag-of-Words (BoW) or Medical Subject Headings (MeSH) index terms. In all five submitted runs, we used manually constructed MeSH queries to filter a target corpus for each of the three clinical question types. Query expansion (for both MeSH and BoW runs) was based on the automatic generation of disease hypotheses for which we used data from OrphaNet [4] and the Disease Symptom Knowledge Database [3]. Our best run was a MeSH-based run in which PubMed was queried directly with the MeSH terms extracted from the case reports, combined with the MeSH terms of the top 5 disease hypotheses generated for the case reports. Compared to the other participants we achieved low scores. Preliminary analysis shows that our corpus filtering method was too strict and has a negative impact on recall.
Document Details
- Document Type
- Technical Report
- Publication Date
- Nov 01, 2014
- Accession Number
- ADA618580
Entities
People
- Aurelie Neveol
- Brigitte Grau
- Eva D'hondt
- Matthieu Schuers
- Pierre Zweigenbaum
- Stefan Darmoni
Organizations
- National Center for Scientific Research