REVIEW 6 cited by
Emergency Department Decision Support using Clinical Pseudo-notes
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Emergency Department Decision Support using Clinical Pseudo-notes
read the original abstract
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.
Forward citations
Cited by 6 Pith papers
-
Expanders Meet Reed-Muller: Easy Instances of Noisy k-XOR
Explicit near-optimal expanders exist for which noisy k-XOR is polynomial-time solvable, falsifying conjectures that expansion implies hardness.
-
Event Fields: Learning Latent Event Structure for Waveform Foundation Models
Event-centric waveform foundation models are learned via self-supervised consistency on latent event structures and interactions, yielding improved performance and label efficiency over sequence-based baselines on phy...
-
Uncertainty-Aware Foundation Models for Clinical Data
The work introduces uncertainty-aware foundation models for clinical data by learning set-valued patient representations that enforce consistency across partial observations and integrate multimodal self-supervised ob...
-
WISTERIA: Learning Clinical Representations from Noisy Supervision via Multi-View Consistency in Electronic Health Records
WISTERIA learns robust clinical representations from noisy EHR labels by enforcing consistency across multiple weak supervision views plus ontology regularization.
-
Predicting Post-Traumatic Epilepsy from Clinical Records using Large Language Model Embeddings
LLM embeddings from clinical records, fused with tabular data via gradient-boosted trees, predict post-traumatic epilepsy at AUC-ROC 0.892 and AUPRC 0.798.
-
Large Language Models as Unified Multimodal Learners for Clinical Prediction
Serializing all patient data — notes, vitals, labs — into one text sequence and fine-tuning an LLM matches or beats task-specific multimodal fusion baselines on mortality, graft-failure, and triage prediction.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.