Scaling Recurrence-aware Foundation Models for Clinical Records via Next-Visit Prediction

• URL: http://arxiv.org/abs/2603.24562v1
• Date: Wed, 25 Mar 2026 17:42:47 GMT
• Title: Scaling Recurrence-aware Foundation Models for Clinical Records via Next-Visit Prediction
• Abstract summary: We present RAVEN, a novel generative pretraining strategy for sequential EHR data based on Recurrence-Aware next-Visit EveNt prediction.<n>Our model learns to autoregressively generate tokenized clinical events for the next visit conditioned on patient history.
• Score: 34.2525943791427
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While large-scale pretraining has revolutionized language modeling, its potential remains underexplored in healthcare with structured electronic health records (EHRs). We present RAVEN, a novel generative pretraining strategy for sequential EHR data based on Recurrence-Aware next-Visit EveNt prediction. Leveraging a dataset of over one million unique individuals, our model learns to autoregressively generate tokenized clinical events for the next visit conditioned on patient history. We introduce regularization on predicting repeated events and highlight a key pitfall in EHR-based foundation model evaluations: repeated event tokens can inflate performance metrics when new onsets are not distinguished from subsequent occurrences. Furthermore, we empirically investigate the scaling behaviors in a data-constrained, compute-saturated regime, showing that simply increasing model size is suboptimal without commensurate increases in data volume. We evaluate our model via zero-shot prediction for forecasting the incidence of a diverse set of diseases, where it rivals fully fine-tuned representation-based Transformer models and outperforms widely used simulation-based next-token approaches. Finally, without additional parameter updates, we show that RAVEN can generalize to an external patient cohort under lossy clinical code mappings and feature coverage gaps.

Related papers

• The Patient is not a Moving Document: A World Model Training Paradigm for Longitudinal EHR [3.9541492005870604]
  We introduce SMB-Structure, a world model for structured EHR that grounds a joint-embedding prediction architecture (JEPA) with next-token prediction (SFT)<n>We validate across two large-scale cohorts: Memorial Sloan Kettering (23,319 oncology patients; 323,000+ patient-years) and INSPECT (19,402 pulmonary embolism patients)
  arXiv  Detail & Related papers  (2026-01-29T18:49:37Z)
• Foundation Models for Clinical Records at Health System Scale [40.88151645546234]
  We present a novel generative pretraining strategy for sequential EHR data using next-visit event prediction.<n>Our model learns to autoregressively generate various tokenized clinical events for the next visit based on patient history.
  arXiv  Detail & Related papers  (2025-07-01T08:52:33Z)
• Lost in Retraining: Roaming the Parameter Space of Exponential Families Under Closed-Loop Learning [0.0]
  We study closed-loop learning for models that belong to exponential families.<n>We show that maximum likelihood of the parameters endows sufficient statistics with the martingale property.<n>We show that this outcome may be prevented if the data contains at least one data point generated from a ground truth model.
  arXiv  Detail & Related papers  (2025-06-25T17:12:22Z)
• Zero-shot Medical Event Prediction Using a Generative Pre-trained Transformer on Electronic Health Records [8.575985305475355]
  Generative pre-trained transformers (GPT) can leverage Longitudinal Data in EHRs to predict future events.<n> fine-tuning of these models can enhance task-specific performance, but it becomes costly when applied to many clinical prediction tasks.<n>A pretrained foundation model can be used in zero-shot forecasting setting, offering a scalable alternative to fine-tuning separate models for each outcome.
  arXiv  Detail & Related papers  (2025-03-07T19:26:47Z)
• Spatial Reasoning with Denoising Models [49.83744014336816]
  We introduce a framework to perform reasoning over sets of continuous variables via denoising generative models.<n>For the first time, that order of generation can successfully be predicted by the denoising network itself.<n>Using these findings, we can increase the accuracy of specific reasoning tasks from 1% to >50%.
  arXiv  Detail & Related papers  (2025-02-28T14:08:30Z)
• Synthesizing Multimodal Electronic Health Records via Predictive Diffusion Models [69.06149482021071]
  We propose a novel EHR data generation model called EHRPD. It is a diffusion-based model designed to predict the next visit based on the current one while also incorporating time interval estimation. We conduct experiments on two public datasets and evaluate EHRPD from fidelity, privacy, and utility perspectives.
  arXiv  Detail & Related papers  (2024-06-20T02:20:23Z)
• MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation [58.93221876843639]
  This paper introduces a novel, end-to-end diffusion-based risk prediction model, named MedDiffusion. It enhances risk prediction performance by creating synthetic patient data during training to enlarge sample space. It discerns hidden relationships between patient visits using a step-wise attention mechanism, enabling the model to automatically retain the most vital information for generating high-quality data.
  arXiv  Detail & Related papers  (2023-10-04T01:36:30Z)
• Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment [72.50906475214457]
  The goal of sequential event prediction is to estimate the next event based on a sequence of historical events. In practice, the next-event prediction models are trained with sequential data collected at one time. We propose a framework with hierarchical branching structures for learning context-specific representations.
  arXiv  Detail & Related papers  (2022-10-24T07:54:13Z)
• Simple Recurrent Neural Networks is all we need for clinical events predictions using EHR data [22.81278657120305]
  Recurrent neural networks (RNNs) are common architecture for EHR-based clinical events predictive models. We used two prediction tasks: the risk for developing heart failure and the risk of early readmission for inpatient hospitalization. We found that simple gated RNN models, including GRUs and LSTMs, often offer competitive results when properly tuned with Bayesian Optimization.
  arXiv  Detail & Related papers  (2021-10-03T13:07:23Z)
• SANSformers: Self-Supervised Forecasting in Electronic Health Records with Attention-Free Models [48.07469930813923]
  This work aims to forecast the demand for healthcare services, by predicting the number of patient visits to healthcare facilities. We introduce SANSformer, an attention-free sequential model designed with specific inductive biases to cater for the unique characteristics of EHR data. Our results illuminate the promising potential of tailored attention-free models and self-supervised pretraining in refining healthcare utilization predictions across various patient demographics.
  arXiv  Detail & Related papers  (2021-08-31T08:23:56Z)
• Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future [73.03458424369657]
  In real-time forecasting in public health, data collection is a non-trivial and demanding task. 'Backfill' phenomenon and its effect on model performance has been barely studied in the prior literature. We formulate a novel problem and neural framework Back2Future that aims to refine a given model's predictions in real-time.
  arXiv  Detail & Related papers  (2021-06-08T14:48:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.