Multi-Modal Contrastive Learning for Online Clinical Time-Series Applications
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 16 of its 28 references that are in the map. First posted on arXiv 27 Mar 2024.
Builds on
16 of 28 references.
References · 16
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
- 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
- 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Modelscited by 6,069
- 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
- 2016Understanding intermediate layers using linear classifier probescited by 1,279
- 2019ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmissioncited by 487
- 2022Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistencycited by 246
- 2023Revisiting Temporal Modeling for CLIP-based Image-to-Video Knowledge Transferringcited by 57
- 2019Using Clinical Notes with Time Series Data for ICU Managementcited by 38
- 2021Neighborhood Contrastive Learning Applied to Online Patient Monitoringcited by 32
- 2023Yet Another ICU Benchmark: A Flexible Multi-Center Framework for Clinical MLcited by 28
- 2023LiDAR: Sensing Linear Probing Performance in Joint Embedding SSL Architecturescited by 27
4 more
- 2022POETREE: Interpretable Policy Learning with Adaptive Decision Treescited by 14
- 2022On the Importance of Clinical Notes in Multi-modal Learning for EHR Datacited by 10
- 2023On the Importance of Step-wise Embeddings for Heterogeneous Clinical Time-Seriescited by 5
- 2023Knowledge Graph Representations to enhance Intensive Care Time-Series Predictionscited by 3
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2 papers.