Point-of-care AI · Active
All researchContinuously monitored vital signs and heart rate variability during labor predict maternal fever 2-3 hours before clinical onset, with area under the curve of 0.748, enabling early detection of mothers at risk for neonatal early-onset sepsis.
2 researchers2 publications2 figures


From the papers
Figures reproduced from the open-access papers behind this project.
Each panel keeps its original source and license. Scroll the column at left to move through the published results while these notes stay in view.
People · 2
Publications · 2
Related
This project develops generative deep learning models to reconstruct multi-lead ECG signals from minimal input leads, assessing whether a small number of key ECG recordings can capture sufficient signal and diagnostic information for robust clinical interpretation and scalable cardiac monitoring.
Point-of-care AI
Machine learning models identify high-risk acute coronary syndrome patients using admission ECG features and vital signs to predict in-hospital mortality and clinical deterioration, with interpretable outputs that guide triage and escalation decisions in acute cardiovascular care.
Point-of-care AI
Developing a machine learning framework to automatically identify delirium from clinical chart text using large language models, and building a predictive model to detect delirium onset early using clinical and physiological biomarkers.
Point-of-care AI