Convolutional Neural Network for Automatic MeSH Indexing

Abstract

MEDLINE is the indexed subset of the National Library of Medicine’s (NLM) journal citation database. It currently contains over 25 million biomedical citations, each indexed with a controlled vocabulary called MeSH. Since 1990, there has been a sizable increase in the number of articles indexed each year for MEDLINE, and since 2002, the NLM has been using automatic MeSH indexing systems to assist indexers with their increasing workload. This paper explores a deep learning approach to the automatic MeSH indexing problem. We present a Convolutional Neural Network (CNN) for automatic MeSH indexing and evaluate its performance by participating in the BioASQ 2019 task on large-scale online biomedical semantic indexing. The CNN model demonstrates competitive performance and outperforms the NLM’s Medical Text Indexer (MTI) by about 3%. The paper presents a preliminary analysis comparing the results of the CNN model to MTI and also outlines the advantages of end-to-end deep learning approaches to automatic MeSH indexing.