Impact of detecting clinical trial elements in exploration of COVID-19 literature
This work addresses the need for efficient biomedical literature exploration during the COVID-19 pandemic, but it is incremental as it builds on existing concept recognition methods.
The study compared standard search engine results with those filtered by clinical trial elements (PICO criteria) for COVID-19 literature, finding that relational concept filtering increased precision and reduced unjudged documents, exposing users to more relevant documents.
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.