Machine Learning Pipeline Integrating Real-time Multimodal Biosignal Sensor Data for Early Detection of Postpartum Hemorrhage

Abstract

Postpartum hemorrhage (PPH) is one of the leading causes of maternal deaths worldwide, especially in low-resource regions where early detection and timely intervention are a challenge. Current methods detecting PPH include visual estimation of blood loss and are often inaccurate, leading to delays in diagnosis and treatment of PPH. This paper will propose an innovative machine learning pipeline that integrates real-time data from wearable biosignal sensors in conjunction with specific electronic medical records (EMRs) to predict the risk of PPH early on. Using non-invasive, real-time physiological monitoring, personal EMR data, and ML algorithms, this system will improve predictive accuracy, improving maternal health outcomes in areas where immediate access to medical care and necessary resources can be limited.