Foundation Models for Clinical Records at Health System Scale

• URL: http://arxiv.org/abs/2507.00574v1
• Date: Tue, 01 Jul 2025 08:52:33 GMT
• Title: Foundation Models for Clinical Records at Health System Scale
• Authors: Haresh Rengaraj Rajamohan, Xiang Gao, Weicheng Zhu, Shih-Lun Huang, Long Chen, Kyunghyun Cho, Cem M. Deniz, Narges Razavian,
• Abstract summary: 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.
• Score: 40.88151645546234
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large-scale pretraining has transformed modeling of language and other data types, but its potential remains underexplored in healthcare with structured electronic health records (EHRs). We present a novel generative pretraining strategy for sequential EHR data using next-visit event prediction. Our model learns to autoregressively generate various tokenized clinical events for the next visit based on patient history and inherently handles the joint prediction of heterogeneous data types. Additionally, 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. Our model is evaluated via zero-shot prediction for forecasting dementia and knee osteoarthritis incidence within 2 and 5 years, and the model performance rivals a fully fine-tuned masked pretrained Transformer baseline, demonstrating that our approach captures complex clinical dependencies without requiring costly task-specific fine-tuning.

Related papers

• Zero-shot Medical Event Prediction Using a Generative Pre-trained Transformer on Electronic Health Records [8.575985305475355]
  We show that a foundation model trained on EHRs can perform predictive tasks in a zero-shot manner.<n>Unlike supervised approaches requiring extensive labeled data, our method enables the model to forecast a next medical event purely from a pretraining knowledge.
  arXiv  Detail & Related papers  (2025-03-07T19:26:47Z)
• Deep State-Space Generative Model For Correlated Time-to-Event Predictions [54.3637600983898]
  We propose a deep latent state-space generative model to capture the interactions among different types of correlated clinical events. Our method also uncovers meaningful insights about the latent correlations among mortality and different types of organ failures.
  arXiv  Detail & Related papers  (2024-07-28T02:42:36Z)
• Recent Advances in Predictive Modeling with Electronic Health Records [71.19967863320647]
  utilizing EHR data for predictive modeling presents several challenges due to its unique characteristics. Deep learning has demonstrated its superiority in various applications, including healthcare.
  arXiv  Detail & Related papers  (2024-02-02T00:31:01Z)
• IGNITE: Individualized GeNeration of Imputations in Time-series Electronic health records [6.630372114304835]
  We propose a novel deep-learning model that learns the underlying patient dynamics to generate personalized values conditioning on an individual's demographic characteristics and treatments.<n>Our proposed model, IGNITE, utilise a conditional dual-variational autoencoder augmented with dual-stage attention to generate missing values for an individual.<n>We show that IGNITE outperforms state-of-the-art approaches in missing data reconstruction and task prediction.
  arXiv  Detail & Related papers  (2024-01-09T07:57:21Z)
• 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)
• 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)
• Pre-training transformer-based framework on large-scale pediatric claims data for downstream population-specific tasks [3.1580072841682734]
  This study presents the Claim Pre-Training (Claim-PT) framework, a generic pre-training model that first trains on the entire pediatric claims dataset. The effective knowledge transfer is completed through the task-aware fine-tuning stage. We conducted experiments on a real-world claims dataset with more than one million patient records.
  arXiv  Detail & Related papers  (2021-06-24T15:25:41Z)
• Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data [62.29031007761901]
  This paper proposes a novel contrastive regularized clinical classification model. We introduce two unique positive sampling strategies specifically tailored for EHR data. Our framework yields highly competitive experimental results in predicting the mortality risk on real-world COVID-19 EHR data.
  arXiv  Detail & Related papers  (2021-04-07T06:02:04Z)

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.