A Review of the Use of Data Analytics to Address Preeclampsia in Ecuador Between 2020 and 2024

Preeclampsia is one of the leading causes of maternal and perinatal morbidity and mortality worldwide. The incidence of preeclampsia in Ecuador is approximately 51 cases per 1000 pregnancies. Despite advances in medicine, its diagnosis and management remain a challenge due to its multifactorial natu...

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Main Authors: Franklin Parrales-Bravo, Lorenzo Cevallos-Torres, Leonel Vasquez-Cevallos, Rosangela Caicedo-Quiroz, Roberto Tolozano-Benites, Víctor Gómez-Rodríguez
Format: Article
Language:English
Published: MDPI AG 2025-04-01
Series:Diagnostics
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Online Access:https://www.mdpi.com/2075-4418/15/8/978
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author Franklin Parrales-Bravo
Lorenzo Cevallos-Torres
Leonel Vasquez-Cevallos
Rosangela Caicedo-Quiroz
Roberto Tolozano-Benites
Víctor Gómez-Rodríguez
author_facet Franklin Parrales-Bravo
Lorenzo Cevallos-Torres
Leonel Vasquez-Cevallos
Rosangela Caicedo-Quiroz
Roberto Tolozano-Benites
Víctor Gómez-Rodríguez
author_sort Franklin Parrales-Bravo
collection DOAJ
description Preeclampsia is one of the leading causes of maternal and perinatal morbidity and mortality worldwide. The incidence of preeclampsia in Ecuador is approximately 51 cases per 1000 pregnancies. Despite advances in medicine, its diagnosis and management remain a challenge due to its multifactorial nature and variability in its clinical presentation. Data analytics offers an innovative approach to address these challenges, allowing for better understanding of the disease and more informed decision-making. This work review examines peer-reviewed studies published during the last decade that employed descriptive, diagnostic, predictive, and prescriptive analytics to evaluate preeclampsia in Ecuador. The review focuses on studies conducted in healthcare institutions across coastal and highland regions, with an inclusion criterion requiring sample sizes greater than 100 patients. Emphasis is placed on the statistical methods used, main findings, and the technological capabilities of the facilities where the analyses were performed. Critical evaluation of methodology limitations and a comparative discussion of findings with global literature on preeclampsia are included. The synthesis of these studies highlights both progress and gaps in predictive analytics for preeclampsia and suggests pathways for future research.
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spelling doaj-art-bb7a59c573064bfdb96dab9118d9e6772025-08-20T03:13:30ZengMDPI AGDiagnostics2075-44182025-04-0115897810.3390/diagnostics15080978A Review of the Use of Data Analytics to Address Preeclampsia in Ecuador Between 2020 and 2024Franklin Parrales-Bravo0Lorenzo Cevallos-Torres1Leonel Vasquez-Cevallos2Rosangela Caicedo-Quiroz3Roberto Tolozano-Benites4Víctor Gómez-Rodríguez5Grupo de Investigación en Inteligencia Artificial, Facultad de Ciencias Matemáticas y Físicas, Universidad de Guayaquil, Guayaquil 090514, EcuadorGrupo de Investigación en Inteligencia Artificial, Facultad de Ciencias Matemáticas y Físicas, Universidad de Guayaquil, Guayaquil 090514, EcuadorSIMUEES Simulation Clinic, Universidad Espíritu Santo, Samborondón 092301, EcuadorCentro de Estudios para el Cuidado Integral y la Promoción de la Salud, Universidad Bolivariana del Ecuador, Km 5 ½ vía Durán—Yaguachi, Durán 092405, EcuadorCentro de Estudios para el Cuidado Integral y la Promoción de la Salud, Universidad Bolivariana del Ecuador, Km 5 ½ vía Durán—Yaguachi, Durán 092405, EcuadorInstituto Superior Tecnológico Urdesa (ITSU), Av. Pdte. Carlos Julio Arosemena Tola Km 2 ½, Guayaquil 090615, EcuadorPreeclampsia is one of the leading causes of maternal and perinatal morbidity and mortality worldwide. The incidence of preeclampsia in Ecuador is approximately 51 cases per 1000 pregnancies. Despite advances in medicine, its diagnosis and management remain a challenge due to its multifactorial nature and variability in its clinical presentation. Data analytics offers an innovative approach to address these challenges, allowing for better understanding of the disease and more informed decision-making. This work review examines peer-reviewed studies published during the last decade that employed descriptive, diagnostic, predictive, and prescriptive analytics to evaluate preeclampsia in Ecuador. The review focuses on studies conducted in healthcare institutions across coastal and highland regions, with an inclusion criterion requiring sample sizes greater than 100 patients. Emphasis is placed on the statistical methods used, main findings, and the technological capabilities of the facilities where the analyses were performed. Critical evaluation of methodology limitations and a comparative discussion of findings with global literature on preeclampsia are included. The synthesis of these studies highlights both progress and gaps in predictive analytics for preeclampsia and suggests pathways for future research.https://www.mdpi.com/2075-4418/15/8/978preeclampsiaclinical decision support systemsdisease diagnosisdata analyticsdescriptivediagnostic
spellingShingle Franklin Parrales-Bravo
Lorenzo Cevallos-Torres
Leonel Vasquez-Cevallos
Rosangela Caicedo-Quiroz
Roberto Tolozano-Benites
Víctor Gómez-Rodríguez
A Review of the Use of Data Analytics to Address Preeclampsia in Ecuador Between 2020 and 2024
Diagnostics
preeclampsia
clinical decision support systems
disease diagnosis
data analytics
descriptive
diagnostic
title A Review of the Use of Data Analytics to Address Preeclampsia in Ecuador Between 2020 and 2024
title_full A Review of the Use of Data Analytics to Address Preeclampsia in Ecuador Between 2020 and 2024
title_fullStr A Review of the Use of Data Analytics to Address Preeclampsia in Ecuador Between 2020 and 2024
title_full_unstemmed A Review of the Use of Data Analytics to Address Preeclampsia in Ecuador Between 2020 and 2024
title_short A Review of the Use of Data Analytics to Address Preeclampsia in Ecuador Between 2020 and 2024
title_sort review of the use of data analytics to address preeclampsia in ecuador between 2020 and 2024
topic preeclampsia
clinical decision support systems
disease diagnosis
data analytics
descriptive
diagnostic
url https://www.mdpi.com/2075-4418/15/8/978
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