Impact of detecting clinical trial elements in exploration of COVID-19 literature

TL;DR

This study evaluates how detecting clinical trial elements like PICO criteria in COVID-19 literature can improve search precision and relevance, aiding more efficient biomedical information retrieval.

Contribution

It provides a comparative analysis showing that concept-based filtering enhances retrieval precision and reduces unjudged documents in COVID-19 literature searches.

Findings

Relational concept filtering increases search precision.

Filtering reduces unjudged documents.

Concept-based approaches improve relevance in literature exploration.

Abstract

The COVID-19 pandemic has driven ever-greater demand for tools which enable efficient exploration of biomedical literature. Although semi-structured information resulting from concept recognition and detection of the defining elements of clinical trials (e.g. PICO criteria) has been commonly used to support literature search, the contributions of this abstraction remain poorly understood, especially in relation to text-based retrieval. In this study, we compare the results retrieved by a standard search engine with those filtered using clinically-relevant concepts and their relations. With analysis based on the annotations from the TREC-COVID shared task, we obtain quantitative as well as qualitative insights into characteristics of relational and concept-based literature exploration. Most importantly, we find that the relational concept selection filters the original retrieved collection in a way that decreases the proportion of unjudged documents and increases the precision, which means that the user is likely to be exposed to a larger number of relevant documents.