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.

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 a...

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Publicado en:	Investigación & Educación en Enfermería Vol. 44; no. 2; pp. 94 - 113
Autores principales:	Devi, Seeta, Sayyed, Faheemuddin, Awasthi, Sanidhya, Sonchhatra, Raghav
Formato:	Artículo
Publicado:	Universidad de Antioquia, Facultad de Enfermeria May-Jul2026
Materias:	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 India
Acceso en línea:	Ver este registro en EBSCOhost