Improving search over Electronic Health Records using UMLS-based query expansion through random walks

ObjectiveMost of the information in Electronic Health Records (EHRs) is represented in free textual form. Practitioners searching EHRs need to phrase their queries carefully, as the record might use synonyms or other related words. In this paper we show that an automatic query expansion method based on the Unified Medicine Language System (UMLS) Metathesaurus improves the results of a robust baseline when searching EHRs.Materials and methodsThe method uses a graph representation of the lexical units, concepts and relations in the UMLS Metathesaurus. It is based on random walks over the graph, which start on the query terms. Random walks are a well-studied discipline in both Web and Knowledge Base datasets.ResultsOur experiments over the TREC Medical Record track show improvements in both the 2011 and 2012 datasets over a strong baseline.DiscussionOur analysis shows that the success of our method is due to the automatic expansion of the query with extra terms, even when they are not directly related in the UMLS Metathesaurus. The terms added in the expansion go beyond simple synonyms, and also add other kinds of topically related terms.ConclusionsExpansion of queries using related terms in the UMLS Metathesaurus beyond synonymy is an effective way to overcome the gap between query and document vocabularies when searching for patient cohorts

Concept oriented biomedical information retrieval

Le domaine biomédical est probablement le domaine où il y a les ressources les plus riches. Dans ces ressources, on regroupe les différentes expressions exprimant un concept, et définit des relations entre les concepts. Ces ressources sont construites pour faciliter l’accès aux informations dans le domaine. On pense généralement que ces ressources sont utiles pour la recherche d’information biomédicale. Or, les résultats obtenus jusqu’à présent sont mitigés : dans certaines études, l’utilisation des concepts a pu augmenter la performance de recherche, mais dans d’autres études, on a plutôt observé des baisses de performance. Cependant, ces résultats restent difficilement comparables étant donné qu’ils ont été obtenus sur des collections différentes. Il reste encore une question ouverte si et comment ces ressources peuvent aider à améliorer la recherche d’information biomédicale. Dans ce mémoire, nous comparons les différentes approches basées sur des concepts dans un même cadre, notamment l’approche utilisant les identificateurs de concept comme unité de représentation, et l’approche utilisant des expressions synonymes pour étendre la requête initiale. En comparaison avec l’approche traditionnelle de "sac de mots", nos résultats d’expérimentation montrent que la première approche dégrade toujours la performance, mais la seconde approche peut améliorer la performance. En particulier, en appariant les expressions de concepts comme des syntagmes stricts ou flexibles, certaines méthodes peuvent apporter des améliorations significatives non seulement par rapport à la méthode de "sac de mots" de base, mais aussi par rapport à la méthode de Champ Aléatoire Markov (Markov Random Field) qui est une méthode de l’état de l’art dans le domaine. Ces résultats montrent que quand les concepts sont utilisés de façon appropriée, ils peuvent grandement contribuer à améliorer la performance de recherche d’information biomédicale. Nous avons participé au laboratoire d’évaluation ShARe/CLEF 2014 eHealth. Notre résultat était le meilleur parmi tous les systèmes participants.Health and biomedical area is probably the area where there are the richest domain resources. In these resources, different expressions are clustered into well defined concepts. They are designed to facilitate public access to the health information and are widely believed to be useful for biomedical information retrieval. However the results of previous works are highly mitigated: in some studies, concepts slightly improve the retrieval performance, while in some others degradations are observed. It is however difficult to compare the results directly due to the fact that they have been performed on different test collections. It is still unclear whether and how medical information retrieval can benefit from these knowledge resources. In this thesis we aim at comparing in the same framework two families of approaches to exploit concepts - using concept IDs as the representation units or using synonymous concept expressions to expand the original query. Compared to a traditional bag-of-words (BOW) baseline, our experiments on test collections show that concept IDs always degrades retrieval effectiveness, whereas the second approach can lead to some improvements. In particular, by matching the concept expressions as either strict or flexible phrases, some methods can lead to significant improvement over the BOW baseline and even over MRF model on most query sets. This study shows experimentally that when concepts are used in a suitable way, it can help improve the effectiveness of medical information retrieval. We participated at the ShARe/CLEF 2014 eHealth Evaluation Lab. Our result was the best among all the participating systems

Advances in knowledge discovery and data mining Part II

19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part II</p