From Readiness to Response: AIDriven Antenatal Risk Assessment (mHealth application) and Machine Learning techniques for Midwives Predicting Maternal and Neonatal Outcomes in Low Resource Healthcare Settings.
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| Title: | From Readiness to Response: AIDriven Antenatal Risk Assessment (mHealth application) and Machine Learning techniques for Midwives Predicting Maternal and Neonatal Outcomes in Low Resource Healthcare Settings. |
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| Alternate Title: | De la preparación a la respuesta: evaluación de riesgos prenatales basada en la inteligencia artificial (mHealth) y técnicas de aprendizaje automático para que las enfermeiras matronas predigan los resultados maternos y neonatales en entornos sanitarios con escasos recursos. Da prontidão à resposta: Avaliação de risco pré-natal baseada em ia (aplicativo mhealth) e técnicas de aprendizado de máquina para parteiras na predição de desfechos maternos e neonatais em ambientes de saúde com recursos limitados. |
| Authors: | Devi, Seeta1 drseetadevi1981@gmail.com, Sayyed, Faheemuddin2 faheemuddinsayyed789@gmail.com, Awasthi, Sanidhya2 sanidhya.awasthi19@gmail.com, Sonchhatra, Raghav2 rsonchhatra49@gmail.com |
| Source: | Investigación & Educación en Enfermería. May-Jul2026, Vol. 44 Issue 2, p94-114. 19p. |
| Subjects: | Mobile apps, Risk assessment, Cross-sectional method, Random forest algorithms, Boosting algorithms, Prediction models, Infant mortality, Receiver operating characteristic curves, Artificial intelligence, Systems development, Statistical sampling, Primary health care, Logistic regression analysis, Pregnancy outcomes, Convolutional neural networks, Descriptive statistics, Prenatal care, Support vector machines, Deep learning, Resource-limited settings, Machine learning, Decision trees, Pregnancy complications, Data analysis software, Sensitivity & specificity (Statistics), Disease risk factors |
| Geographic Terms: | India |
| Abstract (English): | Objective. To assess high-risk pregnancies via AI based mHealth application and to develop innovative machine learning (ML) and deep learning (DL) models to predict maternal and neonatal outcomes. Methods. This study was conducted in two stages; in the first stage, we developed an AI-based mHealth application. In the second stage, data were collected using the mHealth application from an estimated sample of 1010 pregnant women who visited primary health centers in Pune, India, enabling seamless risk categorization into low-, moderate-, and high-risk pregnancy categories. The maternal and neonatal outcomes were predicted using ML and DL models. Evaluated with performance metrics such as AUC-ROC, precision, recall, and F1- Score, aligning with prediction models analysis. Results. The results showed that the prevalence of low, moderate, and highrisk pregnancies was 37.33%, 37.82%, and 24.85%, respectively. Support vector machine (SVM) produced an excellent AUCROC of 99.96, especially in predicting premature labor with 97.36% accuracy and an F1-score of 0.97. Random forest (RF) also performed well, detecting abnormal fetal heart rates with 95.75% accuracy. Bagging and RF excelled in predicting meconium in utero (AUC-ROC: 96.2% and 95.75%) and LSCS (AUC-ROC: 98.81% and 98.51%), respectively. Conclusions. The AI based mHealth application accurately predict the maternal and newborn outcomes, empowering midwives to make timely clinical decisions and reduce maternal and newborn mortality. [ABSTRACT FROM AUTHOR] |
| Abstract (Spanish): | Objetivo. Evaluar los embarazos de alto riesgo mediante una aplicación de mHealth basada en IA y desarrollar modelos innovadores de aprendizaje automático (AA) y aprendizaje profundo (AP) para predecir los resultados maternos y neonatales. Métodos. Este estudio se llevó a cabo en dos fases; en la primera fase, desarrollo de una aplicación de mHealth basada en IA. En la segunda fase, se recopilaron datos mediante la aplicación mHealth de una muestra estimada de 1010 mujeres embarazadas que acudieron a centros de atención primaria en Pune (India), lo que permitió una categorización del riesgo del embarazo en bajo, moderado y alto. Los resultados maternos y neonatales se predijeron utilizando modelos de AA y AP. Se evaluaron con métricas de rendimiento como AUC-ROC, la precisión, y el F1-Score, en consonancia con el análisis de modelos de predicción. Resultados. Los resultados mostraron que la distribución de embarazos de riesgo bajo, moderado y alto fue del 37.33%, 37.82% y 24.85%, respectivamente. La máquina de vectores de soporte produjo un excelente AUC-ROC de 99.96%, especialmente en la predicción del parto prematuro, con una precisión del 97.36% y un F1-score de 0.97. El Random Forest también obtuvo buenos resultados, detectando frecuencias cardíacas fetales anormales con una precisión del 95.75 %. Los métodos de «bagging» y RF se destacaron en la predicción de la presencia de meconio intrauterino (AUC-ROC: 96.2% y 95.75%) y Cesárea (AUC-ROC: 98.81% and 98.51%). Conclusión. La aplicación de mHealth basada en inteligencia artificial predijo con precisión los resultados maternos y neonatales, lo que permite a las enfermeras madronas tomar decisiones clínicas oportunas que permitirían reducir la mortalidad materna y neonatal. [ABSTRACT FROM AUTHOR] |
| Abstract (Portuguese): | Objetivo. Avaliar gestações de alto risco por meio de um aplicativo mHealth baseado em IA e desenvolver modelos inovadores de aprendizado de máquina (AM) e aprendizado profundo (AP) para predizer desfechos maternos e neonatais. Métodos. Este estudo foi conduzido em duas etapas; na primeira etapa, desenvolvemos um aplicativo mHealth baseado em IA. Na segunda etapa, os dados foram coletados por meio do aplicativo mHealth de uma amostra estimada de 1.010 gestantes que visitaram centros de saúde primários em Pune, Índia, possibilitando a categorização contínua em categorias de gestação de baixo, moderado e alto risco. Os desfechos maternos e neonatais foram preditos utilizando modelos de AM e AP. Avaliados com métricas de desempenho como AUC-ROC, precisão, revocação e F1-score, em consonância com a análise de modelos de predição médica. Resultados. Os resultados mostraram que a prevalência de gestações de baixo, moderado e alto risco foi de 37.33%, 37.82% e 24.85%, respectivamente. A máquina de vetores de suporte (MVS) produziu excelente AUC-ROC de 99.96, especialmente na predição de trabalho de parto prematuro, com acurácia de 97.36% e F1-score de 0.97. A floresta aleatória (FA) também apresentou bom desempenho, detectando frequências cardíacas fetais anormais com acurácia de 95.75%. Os métodos de «bagging» e RF se destacaram na previsão da presença de mecônio intrauterino (AUC-ROC: 96.2% e 95.75%) e da cesariana (AUC-ROC: 98.81% e 98.51%), respectivamente. Conclusões. O aplicativo mHealth baseado em IA prediz com precisão os desfechos maternos e neonatais, capacitando as parteiras a tomarem decisões clínicas oportunas e a reduzirem a mortalidade materna e neonatal. [ABSTRACT FROM AUTHOR] |
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| Database: | MedicLatina |
| Abstract: | Objective. To assess high-risk pregnancies via AI based mHealth application and to develop innovative machine learning (ML) and deep learning (DL) models to predict maternal and neonatal outcomes. Methods. This study was conducted in two stages; in the first stage, we developed an AI-based mHealth application. In the second stage, data were collected using the mHealth application from an estimated sample of 1010 pregnant women who visited primary health centers in Pune, India, enabling seamless risk categorization into low-, moderate-, and high-risk pregnancy categories. The maternal and neonatal outcomes were predicted using ML and DL models. Evaluated with performance metrics such as AUC-ROC, precision, recall, and F1- Score, aligning with prediction models analysis. Results. The results showed that the prevalence of low, moderate, and highrisk pregnancies was 37.33%, 37.82%, and 24.85%, respectively. Support vector machine (SVM) produced an excellent AUCROC of 99.96, especially in predicting premature labor with 97.36% accuracy and an F1-score of 0.97. Random forest (RF) also performed well, detecting abnormal fetal heart rates with 95.75% accuracy. Bagging and RF excelled in predicting meconium in utero (AUC-ROC: 96.2% and 95.75%) and LSCS (AUC-ROC: 98.81% and 98.51%), respectively. Conclusions. The AI based mHealth application accurately predict the maternal and newborn outcomes, empowering midwives to make timely clinical decisions and reduce maternal and newborn mortality. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 01205307 |
| DOI: | 10.17533/udea.iee.v44n2e08 |
