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

Barbara S. Lancho Barrantes

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

School of Arch, Tech and Eng

Research output: Chapter in Book/Conference proceeding with ISSN or ISBN › Conference contribution with ISSN or ISBN › peer-review

Abstract

This study analyses SDG 3 (Good Health and Well-being) 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.

Original language	English
Title of host publication	The 20th International Society for Scientometrics and Informetrics (ISSI) Conference
Subtitle of host publication	The Conference will be held in Yerevan, Armenia.
Publication status	Accepted/In press - 20 Mar 2025

Bibliographical note

Not Yet Published

Cite this

@inproceedings{6547f53f84134a02bd63658c721c4c5a,

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

abstract = "This study analyses SDG 3 (Good Health and Well-being) 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.",

author = "{Lancho Barrantes}, {Barbara S.}",

note = "Not Yet Published",

year = "2025",

month = mar,

day = "20",

language = "English",

booktitle = "The 20th International Society for Scientometrics and Informetrics (ISSI) Conference",

}

Machine learning-based model to predict topics contributing to Sustainable Development Goals: A study of Latin American and European Countries. / Lancho Barrantes, Barbara S.
The 20th International Society for Scientometrics and Informetrics (ISSI) Conference: The Conference will be held in Yerevan, Armenia. . 2025.

Research output: Chapter in Book/Conference proceeding with ISSN or ISBN › Conference contribution with ISSN or ISBN › peer-review

TY - GEN

T1 - Machine learning-based model to predict topics contributing to Sustainable Development Goals

T2 - A study of Latin American and European Countries

AU - Lancho Barrantes, Barbara S.

N1 - Not Yet Published

PY - 2025/3/20

Y1 - 2025/3/20

N2 - This study analyses SDG 3 (Good Health and Well-being) 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.

AB - This study analyses SDG 3 (Good Health and Well-being) 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.

UR - https://issi2025.iiap.sci.am/

M3 - Conference contribution with ISSN or ISBN

BT - The 20th International Society for Scientometrics and Informetrics (ISSI) Conference

ER -

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

Abstract

Bibliographical note

Other files and links

Embargoed Document

Fingerprint

Cite this