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Applied Sciences
Volume 16
Issue 12
10.3390/app16125816
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Systematic Review

Machine Learning-Assisted Screening in Systematic Reviews: A Case Study on Pelvic Inflammatory Disease Prevention

by
Martín Daniel Guadarrama-Atrizco
1,
Francisco Javier Prado-Galbarro
2,
Carlos Sánchez-Piedra
3,
Rosa del Carmen Milán-Segovia
1,
Karina Sánchez-Herrera
4 and
Juan Manuel Martínez-Núñez
4,*
1
Department of Pharmacy, Faculty of Chemical Sciences, Universidad Autónoma de San Luis Potosí, San Luis Potosí 78210, Mexico
2
Department of Research, Hospital Infantil de México “Federico Gómez”, Mexico City 06720, Mexico
3
Agencia de Evaluación de Tecnologías Sanitarias (AETS), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain
4
Department of Biological Systems, Universidad Autónoma Metropolitana Unidad Xochimilco, Mexico City 04960, Mexico
*
Author to whom correspondence should be addressed.
Appl. Sci. 2026, 16(12), 5816; https://doi.org/10.3390/app16125816 (registering DOI)
Submission received: 16 March 2026 / Revised: 30 May 2026 / Accepted: 4 June 2026 / Published: 9 June 2026
(This article belongs to the Special Issue Machine Learning in Medicine and Health: Data, Methods and Applications)
Versions Notes

Abstract

This study evaluates ASReview, an open-source machine learning application for study selection in systematic literature reviews, using data from a review of whether screening for sexually transmitted infections reduces the incidence of pelvic inflammatory disease. A systematic literature review was conducted in accordance with the PRISMA guidelines, and manual screening produced a fully labeled dataset that served as the reference standard. ASReview was configured with four machine learning classifiers (Naïve Bayes, Random Forest, Support Vector Machines, and Logistic Regression) and two feature extraction methods (TF-IDF and Doc2Vec). Simulation experiments assessed screening efficiency under sampling-based and heuristic stopping rules. The systematic review suggested that annual screening for sexually transmitted infections may reduce the incidence of pelvic inflammatory disease by up to 40% compared with routine practice, although the evidence base was limited. In the simulation experiments, Naïve Bayes with TF-IDF achieved the highest recall and screening efficiency, particularly in datasets with a low prevalence of relevant records. Conservative stopping rules increased the likelihood of complete retrieval but required greater screening effort. Overall, these findings highlight the limited and heterogeneous evidence on sexually transmitted infection screening for pelvic inflammatory disease prevention and show that ASReview may improve the efficiency of study selection when evaluated within a real systematic review workflow.
Keywords: machine learning; systematic reviews; ASReview; pelvic inflammatory disease; sexually transmitted infections machine learning; systematic reviews; ASReview; pelvic inflammatory disease; sexually transmitted infections

Share and Cite

MDPI and ACS Style

Guadarrama-Atrizco, M.D.; Prado-Galbarro, F.J.; Sánchez-Piedra, C.; Milán-Segovia, R.d.C.; Sánchez-Herrera, K.; Martínez-Núñez, J.M. Machine Learning-Assisted Screening in Systematic Reviews: A Case Study on Pelvic Inflammatory Disease Prevention. Appl. Sci. 2026, 16, 5816. https://doi.org/10.3390/app16125816

AMA Style

Guadarrama-Atrizco MD, Prado-Galbarro FJ, Sánchez-Piedra C, Milán-Segovia RdC, Sánchez-Herrera K, Martínez-Núñez JM. Machine Learning-Assisted Screening in Systematic Reviews: A Case Study on Pelvic Inflammatory Disease Prevention. Applied Sciences. 2026; 16(12):5816. https://doi.org/10.3390/app16125816

Chicago/Turabian Style

Guadarrama-Atrizco, Martín Daniel, Francisco Javier Prado-Galbarro, Carlos Sánchez-Piedra, Rosa del Carmen Milán-Segovia, Karina Sánchez-Herrera, and Juan Manuel Martínez-Núñez. 2026. "Machine Learning-Assisted Screening in Systematic Reviews: A Case Study on Pelvic Inflammatory Disease Prevention" Applied Sciences 16, no. 12: 5816. https://doi.org/10.3390/app16125816

APA Style

Guadarrama-Atrizco, M. D., Prado-Galbarro, F. J., Sánchez-Piedra, C., Milán-Segovia, R. d. C., Sánchez-Herrera, K., & Martínez-Núñez, J. M. (2026). Machine Learning-Assisted Screening in Systematic Reviews: A Case Study on Pelvic Inflammatory Disease Prevention. Applied Sciences, 16(12), 5816. https://doi.org/10.3390/app16125816

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