Supporting Evidence-Based Medicine by Finding Both Relevant and Significant Works

TL;DR

This paper introduces an automated classification model for assigning Levels of Evidence to medical publications, enhancing search relevance and reliability in evidence-based medicine by filtering documents based on empirical strength.

Contribution

It presents a novel classification approach for automatically determining LoE in medical literature, significantly improving retrieval relevance in large-scale medical databases.

Findings

Successfully classified over 26 million MEDLINE documents into LoE categories.

Improved retrieval relevance when using LoE as a search filter.

Demonstrated effectiveness on TREC PM datasets.

Abstract

In this paper, we present a new approach to improving the relevance and reliability of medical IR, which builds upon the concept of Level of Evidence (LoE). LoE framework categorizes medical publications into 7 distinct levels based on the underlying empirical evidence. Despite LoE framework's relevance in medical research and evidence-based practice, only few medical publications explicitly state their LoE. Therefore, we develop a classification model for automatically assigning LoE to medical publications, which successfully classifies over 26 million documents in MEDLINE database into LoE classes. The subsequent retrieval experiments on TREC PM datasets show substantial improvements in retrieval relevance, when LoE is used as a search filter.