University of Oulu

Pita Costa, J., Rei, L., Stopar, L., Fuart, F., Grobelnik, M., Mladenić, D., Novalija, I., Staines, A., Pääkkönen, J., Konttila, J., Bidaurrazaga, J., Belar, O., Henderson, C., Epelde, G., Gabaráin, M. A., Carlin, P., & Wallace, J. (2021). NewsMeSH: A new classifier designed to annotate health news with MeSH headings. Artificial Intelligence in Medicine, 114, 102053.

NewsMeSH : a new classifier designed to annotate health news with MeSH headings

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Author: Costa, Joao Pita1,2; Rei, Luis1; Stopar, Luka1,2;
Organizations: 1Jožef Stefan Institute, Slovenia
2Quintelligence, Slovenia
3Dublin City University, Ireland
4University of Oulu, Finland
5BIOEF, Spain
6Northern Ireland Department of Health, UK
7Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Spain
8Biodonostia, Spain
9Open University, UK
10Ulster University, UK
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.7 MB)
Persistent link:
Language: English
Published: Elsevier, 2021
Publish Date: 2022-03-13


Motivation: In the age of big data, the amount of scientific information available online dwarfs the ability of current tools to support researchers in locating and securing access to the necessary materials. Well-structured open data and the smart systems that make the appropriate use of it are invaluable and can help health researchers and professionals to find the appropriate information by, e.g., configuring the monitoring of information or refining a specific query on a disease.

Methods: We present an automated text classifier approach based on the MEDLINE/MeSH thesaurus, trained on the manual annotation of more than 26 million expert-annotated scientific abstracts. The classifier was developed tailor-fit to the public health and health research domain experts, in the light of their specific challenges and needs. We have applied the proposed methodology on three specific health domains: the Coronavirus, Mental Health and Diabetes, considering the pertinence of the first, and the known relations with the other two health topics.

Results: A classifier is trained on the MEDLINE dataset that can automatically annotate text, such as scientific articles, news articles or medical reports with relevant concepts from the MeSH thesaurus.

Conclusions: The proposed text classifier shows promising results in the evaluation of health-related news. The application of the developed classifier enables the exploration of news and extraction of health-related insights, based on the MeSH thesaurus, through a similar workflow as in the usage of PubMed, with which most health researchers are familiar.

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Series: Artificial intelligence in medicine
ISSN: 0933-3657
ISSN-E: 1873-2860
ISSN-L: 0933-3657
Volume: 114
Article number: 102053
DOI: 10.1016/j.artmed.2021.102053
Type of Publication: A1 Journal article – refereed
Field of Science: 217 Medical engineering
Funding: This work was supported by the European Commission H2020 project MIDAS (G.A. nr. 727721).
EU Grant Number: (727721) MIDAS - Meaningful Integration of Data, Analytics and Services
Dataset Reference: The following is Supplementary data to this article:
Copyright information: © 2021 Elsevier B.V. This manuscript version is made available under the CC-BY-NC-ND 4.0 license