Multi-Modal Contrastive Learning for Online Clinical Time-Series Applications
Keywords: Multi-Modal, Contrastive Learning, Time-Series, Intensive Care, Clinical Time-Series, Health-Care, Online Predictions, Zero-Shot
TL;DR: A multi-modal contrastive learning approach to improve zero-shot predictions on online patient monitoring applications
Abstract: Electronic Health Record (EHR) datasets from Intensive Care Units (ICU) contain a diverse set of data modalities. While prior works have successfully leveraged multiple modalities in supervised settings, we apply advanced self-supervised multi-modal contrastive learning techniques to ICU data, specifically focusing on clinical notes and time-series for clinically relevant online prediction tasks. We introduce a loss function Multi-Modal Neighborhood Contrastive Loss (MM-NCL), a soft neighborhood function, and showcase the excellent linear probe and zero-shot performance of our approach.
Submission Number: 48
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