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Search and Graph Database Technologies for Biomedical Semantic Indexing: Experimental Analysis.

Isabel Segura Bedmar¹, Paloma Martínez¹, Adrián Carruana Martín¹

¹LaBDA Group, Department of Computer Science, Universidad Carlos III de Madrid, Leganés, Spain.

JMIR Medical Informatics

|December 3, 2017

Summary

This study introduces a novel system for automatic biomedical semantic indexing using search engine technology and graph databases to assign Medical Subject Headings (MeSH) to articles, achieving promising results.

Keywords:

Medical Subject Headings information storage and retrieval semantic indexing

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Area of Science:

Biomedical Informatics
Information Retrieval
Computational Linguistics

Background:

Biomedical semantic indexing aids human curators in organizing biomedical literature.
Automated indexing systems are crucial for efficient literature management.

Purpose of the Study:

To develop and describe a system for automatic assignment of Medical Subject Headings (MeSH) to biomedical articles from MEDLINE.
To leverage document similarity for improved MeSH term assignment.

Main Methods:

Documents represented as vectors using search engine indexing and cosine similarity.
A scoring function ranks MeSH terms based on frequency and document similarity.
Graph database utilized for MeSH thesaurus to capture hierarchical relationships and apply curator guidelines.

Main Results:

The system achieved a promising F1 score of 69% on the test dataset.
Demonstrated the effectiveness of combining search and graph database technologies.

Conclusions:

This work pioneers the integration of search and graph database technologies for biomedical semantic indexing.
ElasticSearch offers scalable solutions for indexing large document collections like MEDLINE.
Graph search algorithms can support real-time cataloging of MEDLINE abstracts.