A comparative study of approaches in user-centered health information retrieval

TL;DR

This paper surveys user-centered biomedical information retrieval systems, compares prevalent models like LM and VSM, and finds that language modeling approaches outperform vector space models in effectiveness.

Contribution

It provides a comparative analysis of retrieval models and evaluates the impact of external medical resources in biomedical information retrieval.

Findings

Language Model systems outperform VSM systems in key metrics.

State-of-the-art MAP score was 0.4146.

Language modeling approaches achieved higher effectiveness scores.

Abstract

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.