Emergency Department Decision Support using Clinical Pseudo-notes
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.
Code (1)
Tasks
Text GenerationSimilar Papers 제목 키워드 기반
Development of a Large Language Model-based Multi-Agent Clinical Decision Support System for Korean Triage and Acuity Scale (KTAS)-Based Triage and Treatment Planning in Emergency Departments
Emergency department (ED) overcrowding and the complexity of rapid decision-making in critical care settings pose significant challenges to healthcare systems worldwide. While clinical decision support systems (CDSS) hav…
Decision MakingLanguage ModelingLanguage ModellingLarge Language Model+1Assessing the impact of emergency department short stay units using length-of-stay prediction and discrete event simulation
Accurately predicting hospital length-of-stay at the time a patient is admitted to hospital may help guide clinical decision making and resource allocation. In this study we aim to build a decision support system that pr…
Decision MakingDiagnosticfeature selectionLength-of-Stay predictionSmall Language Models for Emergency Departments Decision Support: A Benchmark Study
Large language models (LLMs) have become increasingly popular in medical domains to assist physicians with a variety of clinical and operational tasks. Given the fast-paced and high-stakes environment of emergency depart…
Benchmarking emergency department triage prediction models with machine learning and large public electronic health records
The demand for emergency department (ED) services is increasing across the globe, particularly during the current COVID-19 pandemic. Clinical triage and risk assessment have become increasingly challenging due to the sho…
BenchmarkingDomain-Adapted Small Language Models for Reliable Clinical Triage
Accurate and consistent Emergency Severity Index (ESI) assignment remains a persistent challenge in emergency departments, where highly variable free-text triage documentation contributes to mistriage and workflow ineffi…
Computational EfficiencyDomain Adaptation