Machine learning-based model to predict topics contributing to Sustainable Development Goals - A study of Latin American and European Countries

Aug 4, 2026

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This study analyzes SDG 3 (Good Health and Wellbeing) research from the top 10 publishing countries in Latin America and Europe using OpenAlex data. Data analysis and machine learning identify regional health priorities and forecast future research trends. Results reveal thematic divergence between regions and emphasize the need for context-aware bibliometric strategies. The approach supports more targeted alignment of research efforts with global development goals by accounting for local health challenges.

LanguageEnglish

CategoryResearch & Report

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Pages2

Reading Time5 min

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Abstract

Introduction

Data and Methods

Results

Machine learning-based model to predict topics contributing to Sustainable Development Goals: A study of Latin American and European Countries Barbara S. Lancho Barrantes [b.lanchobarrantes@brighton.ac.uk](b.lanchobarrantes@brighton.ac.uk) School of Architecture, Technology and Engineering, University of Brighton Brighton BN2 4GJ (United Kingdom) Abstract This study analyses SDG 3 (Good Health and Wellbeing) research from the top 10 publishing countries in Latin America and Europe, using data from OpenAlex. Through data analysis and machine learning, it identifies regional health priorities and predicts future research trends. The findings highlight the thematic divergence between world regions and call for context-aware bibliometric strategies to better support global development goals. Introduction Health is a human right and a cornerstone of physical, mental, and social well-being (WHO, 1949) . Ensuring access to healthcare is not only ethical; it is essential for reducing poverty and fostering inclusive, sustainable development. Yet, health systems worldwide face persistent challenges: underfunding, high out-of-pocket costs, fragmented service delivery, and inequities based on the ability to pay. These issues, compounded by ineffective governance, undermine progress toward universal health coverage and financial protection. The 2030 Agenda for Sustainable Development, adopted by the United Nations in 2015, includes 17 SDGs, with Goal 3 dedicated to ensuring healthy lives and well-being for all. Though interlinked with other goals, SDG 3 plays a pivotal role in shaping global health priorities. Recent bibliometric studies have explored how countries' research aligns with SDG challenges (Yamaguchi et al. 2023)However, much of this work focuses on sectors like business or education, leaving a gap in understanding SDG 3-related research, especially in Global South and Global North countries (Yaqub, et al., 2024) Notably, low-income countries, despite facing the greatest SDG-related challenges, contribute minimally to the research driving global progress (Confraria et al., 2024) . This study uses the OpenAlex (Priem et al.2022) database to examine how countries’ SDG 3 research priorities differ. It highlights the need for contextsensitive bibliometric strategies that account for regional health challenges often overlooked in global analyses. By analysing large-scale publication data, research trends, topics, and collaboration patterns, AI tools can predict emerging health research themes with growing accuracy. The research questions of this study are: 􀂃 How do research priorities differ between Latin America (Global South) and Europe (Global North)? 􀂃 Do both regions focus on similar health challenges with equal intensity? 􀂃 Can machine learning-driven models effectively forecast future research trends? Data and Methods This study draws on data from OpenAlex, a large open-access bibliographic database with over 240 million scholarly works. The study focused on SDG 3: Good Health and Well-being, selecting the top 10 publishing countries from: • Latin America: Brazil, Uruguay, Mexico, Colombia, Chile, Costa Rica, Puerto Rico, Argentina, Ecuador, and Peru. • Europe: United Kingdom, France, Germany, Spain, Netherlands, Switzerland, Italy, Belgium, Sweden, and Poland. OpenAlex leverages a machine learning-based SDGClassifier to assess the relevance of academic publications to the 17 Sustainable Development Goals (SDGs) . Using Natural Language Processing (NLP), the system analyses titles, abstracts, keywords, and citations to understand the content of each publication. The SDG BERT model, a multilingual, multi-label transformer trained on SDG-labeled data (Aurora Query Model v5), then assigns a probability score (ranging from 0 to 1) for each SDG, indicating the publication’s relevance. In addition to SDG tagging, OpenAlex employs an automated topic classification system that assigns each pu

FAQs

What SDG and dataset does the study focus on?

The study focuses on SDG 3 (Good Health and Wellbeing) and uses the OpenAlex database to analyze scholarly works from selected top publishing countries in Latin America and Europe.

How does the study use machine learning to assess SDG 3 relevance and topics?

OpenAlex applies an SDGClassifier using NLP over titles, abstracts, keywords, and citations, producing relevance probability scores for SDGs. It also uses an automated topic classification system assigning each publication to one or more of about 4,500 scientific topics.

What differences in research priorities does the study find between Latin America and Europe?

Europe shows stronger output on advanced medical topics such as cancer and cardiac diseases, while Latin America emphasizes mosquito-borne diseases, mental health, and long-term pandemic effects. COVID-19 remains a shared research focus, but with different emphases.

Aug 4, 2026

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