Retrieval-aligned Tabular Foundation Models Enable Robust Clinical Risk Prediction in Electronic Health Records Under Real-world Constraints
cs.AI
Submitted: 2026-04-02
Updated: 2026-08-25
Terminology
Sources
- EHRMamba: Towards Generalizable and Scalable Foundation Models for Electronic Health Records
- Analyzing and Improving Representations with the Soft Nearest Neighbor Loss
- Count-Based Approaches Remain Strong: A Benchmark Against Transformer and LLM Pipelines on Structured EHR
- Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data
- TabR: Tabular Deep Learning Meets Nearest Neighbors in 2023
- Longitudinal Progression Prediction of Alzheimer's Disease with Tabular Foundation Model
- TabM: Advancing Tabular Deep Learning with Parameter-Efficient Ensembling
- TabArena: A Living Benchmark for Machine Learning on Tabular Data
- A Comprehensive Survey of Foundation Models in Medicine
- An Extensive Data Processing Pipeline for MIMIC-IV
- Large Language Models are Powerful Electronic Health Record Encoders
- TabTransformer: Tabular Data Modeling Using Contextual Embeddings
- Learning to Diagnose with LSTM Recurrent Neural Networks
- Transformers Can Do Bayesian Inference
- IM-Context: In-Context Learning for Imbalanced Regression Tasks
- CORE-BEHRT: A Carefully Optimized and Rigorously Evaluated BEHRT
- CEHR-XGPT: A Scalable Multi-Task Foundation Model for Electronic Health Records
- Scalable In-Context Learning on Tabular Data via Retrieval-Augmented Large Language Models
- Revisiting Nearest Neighbor for Tabular Data: A Deep Tabular Baseline Two Decades Later
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