Hugging Face Trending Papers
DeepAJM: Deep Association Joint Model for Irregularly Sampled data
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arXiv AI
Oct 7
DeepAJM: Deep Association Joint Model for Irregularly Sampled data
arXiv:2610.07388v1 Announce Type: cross Abstract: Joint Models simultaneously model longitudinal and survival outcomes, leveraging patterns in patients' longitudinal trajectory to improve the predict...
Hugging Face Trending Papers
Sep 8
NOAH: Learning the Full Patient Journey. A Longitudinal Multimodal Time-Aware Model for Representation and Forecasting
NOAH is a generative transformer that models the entire multimodal patient journey by integrating bidirectional time and a variational latent space to capture continuous, stochastic clinical trajectories. Trained on over 559 million 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 simulations, yielding strong predictive performance across 15 ICD chapters, 29 comorbidities, and time‑to‑event outcomes.
arXiv AI
Sep 10
NOAH: Learning the Full Patient Journey. A Longitudinal Multimodal Time-Aware Model for Representation and Forecasting
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.
arXiv Machine Learning
3d ago
Are Joint-Embedding Predictive Objectives Effective for Longitudinal EHRs?
Masked language modelling (MLM) remains the dominant pretraining objective for transformer encoders on longitudinal electronic health records (EHRs), but reconstructing individual events can overemphasise noisy observations and fails to encourage learning higher‑level patient states. Joint‑Embedding Predictive Architectures (JEPAs), which predict representations in latent space, were tested on MIMIC‑IV data and, while training stably, consistently underperformed standard MLM. The study found that larger target sizes increase prediction error, yet reducing target size does not improve downstream performance, indicating that stable latent prediction alone is insufficient for competitive EHR representation learning and underscoring the difficulty of defining clinically meaningful latent targets for EHR.
arXiv AI
Sep 24
A Leakage-Aware Multimodal Evaluation Framework for Early Intraoperative Acute Kidney Injury Prediction
The study introduces SynerT, a waveform-only hybrid temporal model that uses a causal dilated TCN and dilated recurrent layers to predict early intraoperative acute kidney injury (AKI). Two extensions, SynerT-MM and SynerT-Stack, incorporate hemodynamic summaries, preoperative covariates, and a leakage-safe stacked ensemble to improve discrimination and calibration. Evaluated on the VitalDB database with a strict 60‑minute prediction window, SynerT-Stack achieved the best performance across AUROC, AUPRC, and F1‑max, and demonstrated the greatest net clinical benefit after recalibration.
arXiv AI
Aug 18
Large language models as synthetic clinical experts to inform longitudinal rare-disease modeling
arXiv:2608. 16507v1 Announce Type: new Abstract: Due to the limited amount of information, modeling longitudinal rare-disease data can benefit from integrating clinical knowledge.