Augmenting multimodal pediatric sleep embeddings with PHATE trajectories, persistent homology, movement descriptors, and EHR improves AUPRC and calibration for predicting desaturation, EEG arousal, hypopnea, and apnea.
Journal of Machine Learning Research , month = jan, pages =
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Uncovering Trajectory and Topological Signatures in Multimodal Pediatric Sleep Embeddings
Augmenting multimodal pediatric sleep embeddings with PHATE trajectories, persistent homology, movement descriptors, and EHR improves AUPRC and calibration for predicting desaturation, EEG arousal, hypopnea, and apnea.