Machine learning-based models for gestational diabetes mellitus prediction before 24–28 weeks of pregnancy: A review

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dc.article.number102378
dc.contributor.authorMennickent D.
dc.contributor.authorGuzman-Gutierrez E.
dc.contributor.authorAraya J.
dc.contributor.authorRodriguez A.
dc.contributor.authorFarias-Jofre M.
dc.date.accessioned2024-01-10T14:26:04Z
dc.date.available2024-01-10T14:26:04Z
dc.date.issued2022
dc.description.abstract© 2022Gestational Diabetes Mellitus (GDM) is a hyperglycemia state that impairs maternal and offspring health, short and long-term. It is usually diagnosed at 24–28 weeks of pregnancy (WP), but at that time the fetal phenotype is already altered. Machine learning (ML)-based models have emerged as an auspicious alternative to predict this pathology earlier, however, they must be validated in different populations before their implementation in routine clinical practice. This review aims to give an overview of the ML-based models that have been proposed to predict GDM before 24–28 WP, with special emphasis on their current validation state and predictive performance. Articles were searched in PubMed. Manuscripts written in English and published before January 1, 2022, were considered. 109 original research studies were selected, and categorized according to the type of variables that their models involved: medical, i.e. clinical and/or biochemical parameters; alternative, i.e. metabolites, peptides or proteins, micro-ribonucleic acid molecules, microbiota genera, or other variables that did not fit into the first category; or mixed, i.e. both medical and alternative data. Only 8.3 % of the reviewed models have had validation in independent studies, with low or moderate performance for GDM prediction. In contrast, several models that lack of independent validation have shown a very high predictive power. The evaluation of these promising models in future independent validation studies would allow to assess their performance on different populations, and continue their way towards clinical implementation. Once settled, ML-based models would help to predict GDM earlier, initiate its treatment timely and prevent its negative consequences on maternal and offspring health.
dc.description.funderDIUBB
dc.description.funderFomento Vinculación Internacional para Instituciones de Investigación Regionales
dc.description.funderMinisterio de Educación del Gobierno de Chile
dc.description.funderFONDECYT
dc.description.funderUniversidad de Concepción
dc.description.funderUniversidad del Bio-Bío
dc.description.funderAgencia Nacional de Investigación y Desarrollo
dc.fechaingreso.objetodigital20-03-2024
dc.fuente.origenScopus
dc.identifier.doi10.1016/j.artmed.2022.102378
dc.identifier.eissn1873-32860
dc.identifier.issn18732860 09333657
dc.identifier.pubmedid36207076
dc.identifier.scopusidSCOPUS_ID:85138820472
dc.identifier.urihttps://doi.org/10.1016/j.artmed.2022.102378
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/80400
dc.information.autorucFacultad de Medicina; Farias Jofre, Marcelo Enrique; S/I; 12286
dc.language.isoen
dc.nota.accesocontenido parcial
dc.publisherElsevier B.V.
dc.relation.ispartofArtificial Intelligence in Medicine
dc.revistaArtificial Intelligence in Medicine
dc.rightsacceso restringido
dc.subjectEarly detection
dc.subjectEarly prediction
dc.subjectGestational diabetes mellitus
dc.subjectMachine learning
dc.subjectMultivariate analysis
dc.subjectValidation
dc.subject.ods03 Good health and well-being
dc.subject.odspa03 Salud y bienestar
dc.titleMachine learning-based models for gestational diabetes mellitus prediction before 24–28 weeks of pregnancy: A review
dc.typereseña
dc.volumen132
sipa.codpersvinculados12286
sipa.indexScopus
sipa.trazabilidadCarga SIPA;09-01-2024
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