Enhancing Medical Literature Retrieval with Biomedical Concepts

Abstract

This study investigates the potential of biomedical concepts – genes, diseases, and chemicals – in enhancing biomedical literature retrieval systems amidst the burgeoning volume of biomedical literature. We compare five traditional sparse approaches against hybrid BERT-based methods, to assess their effectiveness in leveraging biomedical concepts for improved retrieval accuracy. Our research poses critical questions on the necessity and efficiency of biomedical concepts in the era of advanced language models and evaluates if these concepts can further refine retrieval outcomes. Using datasets from Text REtrieval Conference (TREC) Precision Medicine (PM) tracks (2017-2019), which are based on the Medline collection (30+ million biomedical publications) and NDCG@10 (Normalized Discounted Cumulative Gain) for evaluation, we demonstrate that biomedical concepts are indeed helpful for both hybrid and sparse retrieval strategies in the biomedical domain and that the performance of sparse and hybrid methods are comparable. We further demonstrate how our findings can be integrated into a live search system to support clinical practice.

Sameh Frihat

Researcher in the first cohort

My research interests include Information Retrieval, Natural Language Processing, Machine Learning, and Explainable AI.

Norbert Fuhr

Principal Investigator

My research interests include Information Retrieval, Natural Language Processing and Computer Science.