Multi-Modal Contrastive Learning for Online Clinical Time-Series Applications
<|reference_start|>Multi-Modal Contrastive Learning for Online Clinical Time-Series Applications: 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.<|reference_end|>
@article{baldenweg2024multi-modal,
    title={Multi-Modal Contrastive Learning for Online Clinical Time-Series
  Applications},
    author={Fabian Baldenweg, Manuel Burger, Gunnar R"atsch, Rita Kuznetsova},
    journal={arXiv preprint arXiv:2403.18316},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.18316},
    primaryClass={cs.LG}
}