Artificial Intelligence-Assisted Smart Maternal Health Monitoring Device for Early Prediction of High-RiskPregnancy, Maternal Complications, and PersonalizedNursing Care

Abstract

This paper presents a conceptual framework for an artificial intelligence-assisted smart maternal health monitoring device engineered for the early prediction of high-risk pregnancy complications and the delivery of data-driven, personalized nursing care. The system integrates wearable multisensor hardware—specifically designed to track maternal blood pressure, heart rate, oxygen saturation, core skin temperature, and fetal heart rate—with a cloud-based AI analytics engine. Predictive models utilizing Machine Learning algorithms such as Random Forest and XGBoost, alongside Deep Learning architectures like Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, continuously process real-time and longitudinal physiological data streams in conjunction with historical Electronic Health Records (EHR). Explainable AI (XAI) frameworks are embedded to guarantee clinical transparency. The integrated architecture enables the early detection of critical gestational pathologies, including preeclampsia, gestational diabetes, maternal anemia, placental insufficiency, and preterm labour. By dynamically stratifying maternal risk profiles, the platform interfaces with an automated Clinical Decision Support System (CDSS) tailored for obstetric nurses. This digital integration facilitates targeted remote assessments, mitigates alarm fatigue, optimizes clinical workflows, and bridges the gap between remote monitoring and active clinical intervention, empowering nursing professionals to deploy precise, timely, and personalized telehealth interventions.