4 papers · 1 filter
Predicting Fetal Outcomes from Cardiotocography Signals Using a Supervised Variational Autoencoder
John Tolladay, Beth Albert, Gabriel Davis Jones
Objective: To develop and interpret a supervised variational autoencoder (VAE) model for classifying cardiotocography (CTG) signals based on pregnancy outcomes, addressing interpre…
Energy Landscapes Enable Reliable Abstention in Retrieval-Augmented Large Language Models for Healthcare
Ravi Shankar, Sheng Wong, Lin Li +4
Reliable abstention is critical for retrieval-augmented generation (RAG) systems, particularly in safety-critical domains such as women's health, where incorrect answers can lead t…
Large language models surpass domain-specific architectures for antepartum electronic fetal monitoring analysis
Sheng Wong, Ravi Shankar, Beth Albert +1
Foundation models (FMs) and large language models (LLMs) have demonstrated promising generalization across diverse domains for time-series analysis, yet their potential for electro…
CleanCTG: A Deep Learning Model for Multi-Artefact Detection and Reconstruction in Cardiotocography
Sheng Wong, Beth Albert, Gabriel Davis Jones
Cardiotocography (CTG) is essential for fetal monitoring but is frequently compromised by diverse artefacts which obscure true fetal heart rate (FHR) patterns and can lead to misdi…