arXiv AI
DeepAJM: Deep Association Joint Model for Irregularly Sampled data
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DeepAJM: Deep Association Joint Model for Irregularly Sampled data
Joint Models simultaneously model longitudinal and survival outcomes, leveraging patterns in patients' longitudinal trajectory to improve the prediction of survival outcomes. The classical parametric...
arXiv AI
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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.
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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 Machine Learning
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PaReGTA: A Temporally Aware LLM-Based Patient Representation Framework for EHR Analytics
PaReGTA is a large‑language‑model based framework that transforms longitudinal electronic health record events into visit‑level templated text with explicit temporal cues, learns visit embeddings through lightweight contrastive fine‑tuning, and aggregates them into fixed‑dimensional patient representations using hybrid temporal pooling. These representations can be fed into conventional machine‑learning models. In a cohort of 39,088 migraine patients from the All of Us Research Program, PaReGTA combined with LightGBM achieved the highest AUC, accuracy, and F1 scores on a held‑out test set compared to sparse, BERT‑based, and recurrent approaches.
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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.
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Multimodal Empirical Bayes Variational Autoencoders for Joint Longitudinal and Time-to-Event Modeling
arXiv:2607. 13984v1 Announce Type: cross Abstract: Longitudinal tumor measurements, dropout information, and genetic covariates provide complementary information about treatment response, but integrating these data sources within a single population modeling framework remains challenging.