Machine learning applied in maternal and fetal health: A narrative review focused on pregnancy diseases and complications

Autores
Mennickent, Daniela; Rodríguez, Andrés; Opazo, María Cecilia; Riedel, Claudia A.; Castro, Erica; Eriz Salinas, Alma; Appel Rubio, Javiera; Aguayo, Claudio; Damiano, Alicia Ermelinda; Guzmán Gutiérrez, Enrique; Araya, Juan
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Introduction: Machine learning (ML) corresponds to a wide variety of methods that use mathematics, statistics and computational science to learn from multiple variables simultaneously. By means of pattern recognition, ML methods are able to find hidden correlations and accomplish accurate predictions regarding different conditions. ML has been successfully used to solve varied problems in different areas of science, such as psychology, economics, biology and chemistry. Therefore, we wondered how far it has penetrated into the field of obstetrics and gynecology. Aim: To describe the state of art regarding the use of ML in the context of pregnancy diseases and complications. Methodology: Publications were searched in PubMed, Web of Science and Google Scholar. Seven subjects of interest were considered: gestational diabetes mellitus, preeclampsia, perinatal death, spontaneous abortion, preterm birth, cesarean section, and fetal malformations. Current state: ML has been widely applied in all the included subjects. Its uses are varied, the most common being the prediction of perinatal disorders. Other ML applications include (but are not restricted to) biomarker discovery, risk estimation, correlation assessment, pharmacological treatment prediction, drug screening, data acquisition and data extraction. Most of the reviewed articles were published in the last five years. The most employed ML methods in the field are non-linear. Except for logistic regression, linear methods are rarely used. Future challenges: To improve data recording, storage and update in medical and research settings from different realities. To develop more accurate and understandable ML models using data from cutting-edge instruments. To carry out validation and impact analysis studies of currently existing high-accuracy ML models. Conclusion: The use of ML in pregnancy diseases and complications is quite recent, and has increased over the last few years. The applications are varied and point not only to the diagnosis, but also to the management, treatment, and pathophysiological understanding of perinatal alterations. Facing the challenges that come with working with different types of data, the handling of increasingly large amounts of information, the development of emerging technologies, and the need of translational studies, it is expected that the use of ML continue growing in the field of obstetrics and gynecology.
Fil: Mennickent, Daniela. Machine Learning Applied in Biomedicine; Chile. Universidad de Concepción; Chile
Fil: Rodríguez, Andrés. Universidad del Bio Bio; Chile. Machine Learning Applied in Biomedicine; Chile
Fil: Opazo, María Cecilia. Millennium Institute On Immunology And Immunotherapy; Chile. Universidad de Las Américas; Chile
Fil: Riedel, Claudia A.. Universidad Andrés Bello; Chile. Millennium Institute On Immunology And Immunotherapy; Chile
Fil: Castro, Erica. Universidad de Atacama; Chile
Fil: Eriz Salinas, Alma. Universidad de Concepción; Chile
Fil: Appel Rubio, Javiera. Universidad de Concepción; Chile
Fil: Aguayo, Claudio. Universidad de Concepción; Chile
Fil: Damiano, Alicia Ermelinda. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Ciencias Biológicas. Cátedra de Biología Celular y Molecular; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Fisiología y Biofísica Bernardo Houssay. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Fisiología y Biofísica Bernardo Houssay; Argentina
Fil: Guzmán Gutiérrez, Enrique. Machine Learning Applied In Biomedicine; Chile. Universidad de Concepción; Chile
Fil: Araya, Juan. Universidad de Concepción; Chile. Machine Learning Applied In Biomedicine; Chile
Materia
ADVERSE PERINATAL OUTCOMES
ARTIFICIAL INTELLIGENCE
MACHINE LEARNING
PREGNANCY COMPLICATIONS
PREGNANCY DISEASES
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/227723
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/227723
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Machine learning applied in maternal and fetal health: A narrative review focused on pregnancy diseases and complicationsMennickent, DanielaRodríguez, AndrésOpazo, María CeciliaRiedel, Claudia A.Castro, EricaEriz Salinas, AlmaAppel Rubio, JavieraAguayo, ClaudioDamiano, Alicia ErmelindaGuzmán Gutiérrez, EnriqueAraya, JuanADVERSE PERINATAL OUTCOMESARTIFICIAL INTELLIGENCEMACHINE LEARNINGPREGNANCY COMPLICATIONSPREGNANCY DISEASEShttps://purl.org/becyt/ford/3.1https://purl.org/becyt/ford/3Introduction: Machine learning (ML) corresponds to a wide variety of methods that use mathematics, statistics and computational science to learn from multiple variables simultaneously. By means of pattern recognition, ML methods are able to find hidden correlations and accomplish accurate predictions regarding different conditions. ML has been successfully used to solve varied problems in different areas of science, such as psychology, economics, biology and chemistry. Therefore, we wondered how far it has penetrated into the field of obstetrics and gynecology. Aim: To describe the state of art regarding the use of ML in the context of pregnancy diseases and complications. Methodology: Publications were searched in PubMed, Web of Science and Google Scholar. Seven subjects of interest were considered: gestational diabetes mellitus, preeclampsia, perinatal death, spontaneous abortion, preterm birth, cesarean section, and fetal malformations. Current state: ML has been widely applied in all the included subjects. Its uses are varied, the most common being the prediction of perinatal disorders. Other ML applications include (but are not restricted to) biomarker discovery, risk estimation, correlation assessment, pharmacological treatment prediction, drug screening, data acquisition and data extraction. Most of the reviewed articles were published in the last five years. The most employed ML methods in the field are non-linear. Except for logistic regression, linear methods are rarely used. Future challenges: To improve data recording, storage and update in medical and research settings from different realities. To develop more accurate and understandable ML models using data from cutting-edge instruments. To carry out validation and impact analysis studies of currently existing high-accuracy ML models. Conclusion: The use of ML in pregnancy diseases and complications is quite recent, and has increased over the last few years. The applications are varied and point not only to the diagnosis, but also to the management, treatment, and pathophysiological understanding of perinatal alterations. Facing the challenges that come with working with different types of data, the handling of increasingly large amounts of information, the development of emerging technologies, and the need of translational studies, it is expected that the use of ML continue growing in the field of obstetrics and gynecology.Fil: Mennickent, Daniela. Machine Learning Applied in Biomedicine; Chile. Universidad de Concepción; ChileFil: Rodríguez, Andrés. Universidad del Bio Bio; Chile. Machine Learning Applied in Biomedicine; ChileFil: Opazo, María Cecilia. Millennium Institute On Immunology And Immunotherapy; Chile. Universidad de Las Américas; ChileFil: Riedel, Claudia A.. Universidad Andrés Bello; Chile. Millennium Institute On Immunology And Immunotherapy; ChileFil: Castro, Erica. Universidad de Atacama; ChileFil: Eriz Salinas, Alma. Universidad de Concepción; ChileFil: Appel Rubio, Javiera. Universidad de Concepción; ChileFil: Aguayo, Claudio. Universidad de Concepción; ChileFil: Damiano, Alicia Ermelinda. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Ciencias Biológicas. Cátedra de Biología Celular y Molecular; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Fisiología y Biofísica Bernardo Houssay. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Fisiología y Biofísica Bernardo Houssay; ArgentinaFil: Guzmán Gutiérrez, Enrique. Machine Learning Applied In Biomedicine; Chile. Universidad de Concepción; ChileFil: Araya, Juan. Universidad de Concepción; Chile. Machine Learning Applied In Biomedicine; ChileFrontiers Media2023-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/227723Mennickent, Daniela; Rodríguez, Andrés; Opazo, María Cecilia; Riedel, Claudia A.; Castro, Erica; et al.; Machine learning applied in maternal and fetal health: A narrative review focused on pregnancy diseases and complications; Frontiers Media; Frontiers in Endocrinology; 14; 1130139; 5-2023; 1-221664-2392CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.3389/fendo.2023.1130139info:eu-repo/semantics/altIdentifier/url/https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2023.1130139/fullinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T14:50:21Zoai:ri.conicet.gov.ar:11336/227723instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-15 14:50:21.718CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Machine learning applied in maternal and fetal health: A narrative review focused on pregnancy diseases and complications
title Machine learning applied in maternal and fetal health: A narrative review focused on pregnancy diseases and complications
spellingShingle Machine learning applied in maternal and fetal health: A narrative review focused on pregnancy diseases and complications
Mennickent, Daniela
ADVERSE PERINATAL OUTCOMES
ARTIFICIAL INTELLIGENCE
MACHINE LEARNING
PREGNANCY COMPLICATIONS
PREGNANCY DISEASES
title_short Machine learning applied in maternal and fetal health: A narrative review focused on pregnancy diseases and complications
title_full Machine learning applied in maternal and fetal health: A narrative review focused on pregnancy diseases and complications
title_fullStr Machine learning applied in maternal and fetal health: A narrative review focused on pregnancy diseases and complications
title_full_unstemmed Machine learning applied in maternal and fetal health: A narrative review focused on pregnancy diseases and complications
title_sort Machine learning applied in maternal and fetal health: A narrative review focused on pregnancy diseases and complications
dc.creator.none.fl_str_mv Mennickent, Daniela
Rodríguez, Andrés
Opazo, María Cecilia
Riedel, Claudia A.
Castro, Erica
Eriz Salinas, Alma
Appel Rubio, Javiera
Aguayo, Claudio
Damiano, Alicia Ermelinda
Guzmán Gutiérrez, Enrique
Araya, Juan
author Mennickent, Daniela
author_facet Mennickent, Daniela
Rodríguez, Andrés
Opazo, María Cecilia
Riedel, Claudia A.
Castro, Erica
Eriz Salinas, Alma
Appel Rubio, Javiera
Aguayo, Claudio
Damiano, Alicia Ermelinda
Guzmán Gutiérrez, Enrique
Araya, Juan
author_role author
author2 Rodríguez, Andrés
Opazo, María Cecilia
Riedel, Claudia A.
Castro, Erica
Eriz Salinas, Alma
Appel Rubio, Javiera
Aguayo, Claudio
Damiano, Alicia Ermelinda
Guzmán Gutiérrez, Enrique
Araya, Juan
author2_role author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv ADVERSE PERINATAL OUTCOMES
ARTIFICIAL INTELLIGENCE
MACHINE LEARNING
PREGNANCY COMPLICATIONS
PREGNANCY DISEASES
topic ADVERSE PERINATAL OUTCOMES
ARTIFICIAL INTELLIGENCE
MACHINE LEARNING
PREGNANCY COMPLICATIONS
PREGNANCY DISEASES
purl_subject.fl_str_mv https://purl.org/becyt/ford/3.1
https://purl.org/becyt/ford/3
dc.description.none.fl_txt_mv Introduction: Machine learning (ML) corresponds to a wide variety of methods that use mathematics, statistics and computational science to learn from multiple variables simultaneously. By means of pattern recognition, ML methods are able to find hidden correlations and accomplish accurate predictions regarding different conditions. ML has been successfully used to solve varied problems in different areas of science, such as psychology, economics, biology and chemistry. Therefore, we wondered how far it has penetrated into the field of obstetrics and gynecology. Aim: To describe the state of art regarding the use of ML in the context of pregnancy diseases and complications. Methodology: Publications were searched in PubMed, Web of Science and Google Scholar. Seven subjects of interest were considered: gestational diabetes mellitus, preeclampsia, perinatal death, spontaneous abortion, preterm birth, cesarean section, and fetal malformations. Current state: ML has been widely applied in all the included subjects. Its uses are varied, the most common being the prediction of perinatal disorders. Other ML applications include (but are not restricted to) biomarker discovery, risk estimation, correlation assessment, pharmacological treatment prediction, drug screening, data acquisition and data extraction. Most of the reviewed articles were published in the last five years. The most employed ML methods in the field are non-linear. Except for logistic regression, linear methods are rarely used. Future challenges: To improve data recording, storage and update in medical and research settings from different realities. To develop more accurate and understandable ML models using data from cutting-edge instruments. To carry out validation and impact analysis studies of currently existing high-accuracy ML models. Conclusion: The use of ML in pregnancy diseases and complications is quite recent, and has increased over the last few years. The applications are varied and point not only to the diagnosis, but also to the management, treatment, and pathophysiological understanding of perinatal alterations. Facing the challenges that come with working with different types of data, the handling of increasingly large amounts of information, the development of emerging technologies, and the need of translational studies, it is expected that the use of ML continue growing in the field of obstetrics and gynecology.
Fil: Mennickent, Daniela. Machine Learning Applied in Biomedicine; Chile. Universidad de Concepción; Chile
Fil: Rodríguez, Andrés. Universidad del Bio Bio; Chile. Machine Learning Applied in Biomedicine; Chile
Fil: Opazo, María Cecilia. Millennium Institute On Immunology And Immunotherapy; Chile. Universidad de Las Américas; Chile
Fil: Riedel, Claudia A.. Universidad Andrés Bello; Chile. Millennium Institute On Immunology And Immunotherapy; Chile
Fil: Castro, Erica. Universidad de Atacama; Chile
Fil: Eriz Salinas, Alma. Universidad de Concepción; Chile
Fil: Appel Rubio, Javiera. Universidad de Concepción; Chile
Fil: Aguayo, Claudio. Universidad de Concepción; Chile
Fil: Damiano, Alicia Ermelinda. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Ciencias Biológicas. Cátedra de Biología Celular y Molecular; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Fisiología y Biofísica Bernardo Houssay. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Fisiología y Biofísica Bernardo Houssay; Argentina
Fil: Guzmán Gutiérrez, Enrique. Machine Learning Applied In Biomedicine; Chile. Universidad de Concepción; Chile
Fil: Araya, Juan. Universidad de Concepción; Chile. Machine Learning Applied In Biomedicine; Chile
description Introduction: Machine learning (ML) corresponds to a wide variety of methods that use mathematics, statistics and computational science to learn from multiple variables simultaneously. By means of pattern recognition, ML methods are able to find hidden correlations and accomplish accurate predictions regarding different conditions. ML has been successfully used to solve varied problems in different areas of science, such as psychology, economics, biology and chemistry. Therefore, we wondered how far it has penetrated into the field of obstetrics and gynecology. Aim: To describe the state of art regarding the use of ML in the context of pregnancy diseases and complications. Methodology: Publications were searched in PubMed, Web of Science and Google Scholar. Seven subjects of interest were considered: gestational diabetes mellitus, preeclampsia, perinatal death, spontaneous abortion, preterm birth, cesarean section, and fetal malformations. Current state: ML has been widely applied in all the included subjects. Its uses are varied, the most common being the prediction of perinatal disorders. Other ML applications include (but are not restricted to) biomarker discovery, risk estimation, correlation assessment, pharmacological treatment prediction, drug screening, data acquisition and data extraction. Most of the reviewed articles were published in the last five years. The most employed ML methods in the field are non-linear. Except for logistic regression, linear methods are rarely used. Future challenges: To improve data recording, storage and update in medical and research settings from different realities. To develop more accurate and understandable ML models using data from cutting-edge instruments. To carry out validation and impact analysis studies of currently existing high-accuracy ML models. Conclusion: The use of ML in pregnancy diseases and complications is quite recent, and has increased over the last few years. The applications are varied and point not only to the diagnosis, but also to the management, treatment, and pathophysiological understanding of perinatal alterations. Facing the challenges that come with working with different types of data, the handling of increasingly large amounts of information, the development of emerging technologies, and the need of translational studies, it is expected that the use of ML continue growing in the field of obstetrics and gynecology.
publishDate 2023
dc.date.none.fl_str_mv 2023-05
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/227723
Mennickent, Daniela; Rodríguez, Andrés; Opazo, María Cecilia; Riedel, Claudia A.; Castro, Erica; et al.; Machine learning applied in maternal and fetal health: A narrative review focused on pregnancy diseases and complications; Frontiers Media; Frontiers in Endocrinology; 14; 1130139; 5-2023; 1-22
1664-2392
CONICET Digital
CONICET
url http://hdl.handle.net/11336/227723
identifier_str_mv Mennickent, Daniela; Rodríguez, Andrés; Opazo, María Cecilia; Riedel, Claudia A.; Castro, Erica; et al.; Machine learning applied in maternal and fetal health: A narrative review focused on pregnancy diseases and complications; Frontiers Media; Frontiers in Endocrinology; 14; 1130139; 5-2023; 1-22
1664-2392
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.3389/fendo.2023.1130139
info:eu-repo/semantics/altIdentifier/url/https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2023.1130139/full
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Frontiers Media
publisher.none.fl_str_mv Frontiers Media
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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