arXiv:2603.08459v2 Announce Type: replace
Abstract: Safe predictions are a crucial requirement for integrating predictive models into clinical decision support systems. One approach to improving trus...
By L. Juli\'an Lechuga L\'opez, Tim G. J. Rudner, Farah E. Shamout
The paper presents a newly curated, multi-center, multi-modal, and longitudinal lung cancer dataset comprising 1,365 patients with whole-slide images, CT scans, PET scans, structured clinical data, transcriptomics, and follow-up information. The dataset features substantial, non-uniform missingness across modalities, making it ideal for evaluating robust multi-modal fusion strategies. Benchmarks on 12‑month overall survival, disease‑specific survival, and longitudinal hazard prediction demonstrate that integrating complementary modalities consistently outperforms uni-modal approaches, even under severe missing data.
By Rita Cordeiro Mendes, Maria Rita Fonseca Verdelho, Carlos Santiago, Catarina Barata
arXiv:2506. 04831v3 Announce Type: replace Abstract: Forecasting how a patient's condition is likely to evolve, including possible deterioration, recovery, treatment needs, and care transitions, could support more proactive and personalized care, but requires modeling heterogeneous and longitudinal electronic health record (EHR) data.
By Chantal Pellegrini, Ege \"Ozsoy, David Bani-Harouni, Matthias Keicher, Nassir Navab
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.
By Minjun Kim, Jong Hak Moon
MultiSigBERT is a unified framework that performs multimodal sequential survival modeling in oncology by integrating narrative clinical reports, numerical measurements, and structured variables. The method converts free-text reports into sentence embeddings, compresses them with modality-specific PCA, and concatenates them with structured covariates to create joint temporal trajectories. These trajectories are encoded using the Signature transform from Rough Paths theory, and the resulting high-dimensional features are fed into a LASSO-regularized Cox model, achieving a concordance index of 0.743 on an independent test set of over 2,500 patients.
By Paul Minchella, St\'ephane Chr\'etien, Guillaume Metzler, Lo\"ic Verlingue, R\'emi Vaucher
NOAH is a generative transformer that learns the full multimodal patient journey by integrating bidirectional time and a variational latent space. Trained on over 559 million clinical events from 431,000 hospital visits, it processes medical images, time‑series, numeric signals, categorical events, and both structured and unstructured records. The model supports autoregressive forecasting, zero‑shot classification, and counterfactual intervention simulation, yielding strong performance on clinical outcomes, ICD chapters, comorbidities, and time‑to‑event prediction.
By Tobias Susetzky, Raphael Rehms, Dmitrii Seletkov, \"Ozg\"un Turgut, Michelle Espranita Liman, Lisa Steinhelfer, Rickmer Braren, Daniel Rueckert