Interpretable Predictive Models to Understand Risk Factors for Maternal and Fetal Outcomes

Bosschieter, Tomas M.; Xu, Zifei; Lan, Hui; Lengerich, Benjamin J.; Nori, Harsha; Painter, Ian; Souter, Vivienne; Caruana, Rich

doi:10.1007/s41666-023-00151-4

Cited by 2 publications

(1 citation statement)

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“…EBMs offer advantages in that they rank and score the risk factors for each outcome they validate. 61 Integrated electronic health record data coupled with genetic information and synergized with polygenic risk scores for systolic blood pressure, prove valuable for the predictive modeling of preeclampsia. ML techniques such as XGBoost and linear regression contribute to the effectiveness of these predictive models.…”

Section: Ai For Fetal Monitoringmentioning

confidence: 99%

A Theoretical Exploration of Artificial Intelligence’s Impact on Feto-Maternal Health from Conception to Delivery

Yaseen,

Rather

2024

IJWH

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The implementation of Artificial Intelligence (AI) in healthcare is enhancing diagnostic accuracy in clinical setups. The use of AI in healthcare is steadily increasing with advancing technology, extending beyond disease diagnosis to encompass roles in fetomaternal health. AI harnesses Machine Learning (ML), Natural Language Processing (NLP), Artificial Neural Networks (ANN), and computer vision to analyze data and draw conclusions. Considering maternal health, ML analyzes vast datasets to predict maternal and fetal health outcomes, while NLP interprets medical texts and patient records to assist in diagnosis and treatment decisions. ANN models identify patterns in complex feto-maternal medical data, aiding in risk assessment and intervention planning whereas, computer vision enables the analysis of medical images for early detection of feto-maternal complications. AI facilitates early pregnancy detection, genetic screening, and continuous monitoring of maternal health parameters, providing real-time alerts for deviations, while also playing a crucial role in the early detection of fetal abnormalities through enhanced ultrasound imaging, contributing to informed decision-making. This review investigates into the application of AI, particularly through predictive models, in addressing the monitoring of feto-maternal health. Additionally, it examines potential future directions and challenges associated with these applications.

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Section: Ai For Fetal Monitoringmentioning

confidence: 99%