mlsanityData to 7 Oct 2026

Emergency Department Decision Support using Clinical Pseudo-notes

arXiv 2402.00160 ↗First posted 31 Jan 2024cs.CLcited by 22

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Abstract (arXiv)

In this work, we introduce the Multiple Embedding Model for EHR (MEME), an approach that serializes multimodal EHR tabular data into text using pseudo-notes, mimicking clinical text generation. This conversion not only preserves better representations of categorical data and learns contexts but also enables the effective employment of pretrained foundation models for rich feature representation. To address potential issues with context length, our framework encodes embeddings for each EHR modality separately. We demonstrate the effectiveness of MEME by applying it to several decision support tasks within the Emergency Department across multiple hospital systems. Our findings indicate that MEME outperforms traditional machine learning, EHR-specific foundation models, and general LLMs, highlighting its potential as a general and extendible EHR representation strategy.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). Builds on 31 of its 55 references that are in the map. First posted on arXiv 31 Jan 2024.

Builds on

31 of 55 references.

References · 31

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19 more
  1. 2019ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmissioncited by 487
  2. 2022TabLLM: Few-shot Classification of Tabular Data with Large Language Modelscited by 273
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  5. 2023EHRSHOT: An EHR Benchmark for Few-Shot Evaluation of Foundation Modelscited by 101
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 10; 2026: 11.

22 papers.

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  1. 2025Clinical ModernBERT: An efficient and long context encoder for biomedical textcited by 26
  2. 2024Can Large Language Models abstract Medical Coded Language?cited by 20
  3. 2025A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMscited by 13
  4. 2025ChronoFormer: Time-Aware Transformer Architectures for Structured Clinical Event Modelingcited by 11
  5. 2025Structured Semantics from Unstructured Notes: Language Model Approaches to EHR-Based Decision Supportcited by 7
  6. 2025FoMoH: A clinically meaningful foundation model evaluation for structured electronic health recordscited by 6
  7. 2026One Loss to Rule Them All: Marked Time-to-Event for Structured EHR Foundation Modelscited by 5
  8. 2026Wavelet-Driven Masked Multiscale Reconstruction for PPG Foundation Modelscited by 4
  9. 2025Time-Aware Attention for Enhanced Electronic Health Records Modelingcited by 3
  10. 2025EHR2Path: Comprehensive Pathway-Level Modeling of Longitudinal Patient Trajectories from Multimodal Electronic Health Recordscited by 2
  11. 2026Learning Clinical Representations Under Systematic Distribution Shiftcited by 2
  12. 2026Discriminative Representation Learning for Clinical Predictioncited by 2
10 more
  1. 2026Retrieval-aligned Tabular Foundation Models Enable Robust Clinical Risk Prediction in Electronic Health Records Under Real-world Constraintscited by 2
  2. 2025Temporal Entailment Pretraining for Clinical Language Models over EHR Datacited by 1
  3. 2025The Multimodal Paradox: How Added and Missing Modalities Shape Bias and Performance in Multimodal AIcited by 1
  4. 2025KEEP: Integrating Medical Ontologies with Clinical Data for Robust Code Embeddingscited by 1
  5. 2026Learning Longitudinal Health Representations from EHR and Wearable Datacited by 1
  6. 2026Uncertainty-Aware Foundation Models for Clinical Datacited by 1
  7. 2026Predicting Post-Traumatic Epilepsy from Clinical Records using Large Language Model Embeddingscited by 1
  8. 2026Dense Feature Learning via Linear Structure Preservation in Medical Datacited by 0
  9. 2026Event Fields: Learning Latent Event Structure for Waveform Foundation Modelscited by 0
  10. 2026WISTERIA: Learning Clinical Representations from Noisy Supervision via Multi-View Consistency in Electronic Health Recordscited by 0