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Multimodal Prompt Learning with Irregular EHRs for Robust Monitoring of Critical Care Patients

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arXiv AI

Aug 25

Multimodal Prompt Learning with Irregular EHRs for Robust Monitoring of Critical Care Patients

arXiv:2608.21941v1 Announce Type: new Abstract: Accurate assessment of patients in intensive care units (ICUs) is essential for timely clinical intervention and improved patient outcomes. Multimodal...

By Yixin Yang, Yueyang Sun, Weichen Liu, Xianbing Zhao, Sicen Liu

multimodal benchmarks

arXiv AI

Jun 10

LongMoE: Longitudinal Multimodal Learning via Trajectory-Aware Mixture-of-Experts

arXiv:2606. 09907v1 Announce Type: cross Abstract: Multimodal clinical learning is increasingly important for integrating diverse patient data, including imaging, text, and personalised health records.

By Maxx Richard Rahman, Prakhar Kumar, Wolfgang Maass

nlp multimodal

arXiv Machine Learning

Jun 5

PAMF: Prior-Aware Multimodal Fusion for Incomplete Time Series Data

arXiv:2606. 06328v1 Announce Type: new Abstract: In healthcare, multimodal time series tasks often operate on incomplete observations in practice, for example when ECG segments are lost because electrodes detach or an entire respiratory channel is unavailable during overnight monitoring.

By Ziwen Kan, Wugeng Zheng, Tianlong Chen, Song Wang

rag diffusion multimodal benchmarks

arXiv AI

Jun 6

TRACE: A Temporal Conditional Estimation for Multimodal Time Series Foundation Models

arXiv:2606. 06285v1 Announce Type: new Abstract: Time series foundation models (TS-FMs) aim to learn generalizable temporal representations that can be adapted to a wide range of downstream tasks.

By Ziwen Kan, Yishuo Chen, Kecheng Li, Andrew Wen, Xiaomeng Wang, Liwei Wang, Jihao Duan, Song Wang, Hongfang Liu, Tianlong Chen

nlp multimodal benchmarks

arXiv Machine Learning

Jul 14

Multimodal Routing for Interpretable, Robust, and Auditable Clinical Prediction

arXiv:2607. 09982v1 Announce Type: new Abstract: Electronic health record (EHR) data are inherently multimodal, and leveraging multiple modalities can improve predictive performance.

By Nikkie Hooman, Zhongjie Wu, Eric C. Larson, Mehak Gupta

multimodal safety

arXiv Machine Learning

Aug 14

Represent, Then Generate: Multimodal-Conditioned Time-Series Generation under Irregular Missingness

arXiv:2608. 12592v1 Announce Type: new Abstract: Continuous physiological time series underpin modern clinical monitoring, yet many of the most informative signals are invasive, expensive, or simply unavailable for a given patient.

By Haochen Zhang, Jiaheng Guo, Yu-Chao Huang, Nicholas Knoz, Tianlong Chen

multimodal benchmarks

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Related stories

Multimodal Prompt Learning with Irregular EHRs for Robust Monitoring of Critical Care Patients

LongMoE: Longitudinal Multimodal Learning via Trajectory-Aware Mixture-of-Experts

PAMF: Prior-Aware Multimodal Fusion for Incomplete Time Series Data

TRACE: A Temporal Conditional Estimation for Multimodal Time Series Foundation Models

Multimodal Routing for Interpretable, Robust, and Auditable Clinical Prediction

Represent, Then Generate: Multimodal-Conditioned Time-Series Generation under Irregular Missingness