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...
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.
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.
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.
arXiv:2607. 09982v1 Announce Type: new Abstract: Electronic health record (EHR) data are inherently multimodal, and leveraging multiple modalities can improve predictive performance.
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.
arXiv:2607. 22264v1 Announce Type: new Abstract: Autoregressive foundation models trained on tokenized electronic health records (EHRs) can support zero-shot clinical prediction, yet most operate on structured event codes alone, and do not incorporate multiple modalities in a principled way.
arXiv:2606. 17106v1 Announce Type: new Abstract: Laboratory tests in electronic health records are collected irregularly, and the absence of a test order can be as informative as the measurement itself.
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. Conditional generation offers a remedy: an absent signal can be synthesized from co-recorded signals and routine clinical variables.
Mr.Dec is a new Transformer‑decoder model that predicts 30‑day hospital readmission by treating each admission as a chronological sequence of daily multimodal events, integrating Electronic Health Record updates and Chest X‑ray findings. It uses disease‑specific supervised contrastive learning to shape a diagnosis‑aware latent space and preserves day‑level clinical signals that other methods often compress. Experiments on MIMIC‑IV and MIMIC‑CXR datasets show state‑of‑the‑art performance and the model can highlight "Critical Days" for actionable real‑time risk stratification.
FedCoRe is a federated learning framework that addresses missing modalities in healthcare by learning representation- or logit-space corrections instead of generating synthetic data. In a MIMIC-derived respiratory deterioration task, the method uses paired examples where a modality is present during training but may be absent at deployment, allowing only those clients to update the completion module. Experiments show that FedCoRe can recover roughly half of the performance lost when ECG or CXR data are hidden, but the framework should only be deployed when paired examples and validation evidence confirm the modality’s presence.
arXiv:2608. 03611v1 Announce Type: new Abstract: Multimodal Sentiment Analysis (MSA) integrates text, audio, and vision to infer human affect, yet real-world multimodal observations are often incomplete.