Multi-Modal Contrastive Learning for Online Clinical Time-Series Applications
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911816298266624 |
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| author | Baldenweg, Fabian Burger, Manuel Rätsch, Gunnar Kuznetsova, Rita |
| author_facet | Baldenweg, Fabian Burger, Manuel Rätsch, Gunnar Kuznetsova, Rita |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_18316 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-Modal Contrastive Learning for Online Clinical Time-Series Applications Baldenweg, Fabian Burger, Manuel Rätsch, Gunnar Kuznetsova, Rita Machine Learning 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. |
| title | Multi-Modal Contrastive Learning for Online Clinical Time-Series Applications |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2403.18316 |