A comparative study of approaches in user-centered health information retrieval
This is an incremental study that benchmarks retrieval methods for biomedical information, aiding researchers in selecting effective models.
This paper compares user-centered health information retrieval systems, focusing on Language Model (LM) and Vector Space Model (VSM) approaches, finding that LM-based systems outperform VSM-based ones with scores like MAP of 0.4146 and P@10 of 0.7560.
In this paper, we survey various user-centered or context-based biomedical health information retrieval systems. We present and discuss the performance of systems submitted in CLEF eHealth 2014 Task 3 for this purpose. We classify and focus on comparing the two most prevalent retrieval models in biomedical information retrieval namely: Language Model (LM) and Vector Space Model (VSM). We also report on the effectiveness of using external medical resources and ontologies like MeSH, Metamap, UMLS, etc. We observed that the L.M. based retrieval systems outperform VSM based systems on various fronts. From the results we conclude that the state-of-art system scores for MAP was 0.4146, P@10 was 0.7560 and NDCG@10 was 0.7445, respectively. All of these score were reported by systems built on language modelling approaches.