REALM: RAG-Driven Enhancement of Multimodal Electronic Health Records Analysis via Large Language Models
The integration of multimodal Electronic Health Records (EHR) data has significantly
improved clinical predictive capabilities. Leveraging clinical notes and multivariate time …
improved clinical predictive capabilities. Leveraging clinical notes and multivariate time …
Is larger always better? Evaluating and prompting large language models for non-generative medical tasks
The use of Large Language Models (LLMs) in medicine is growing, but their ability to handle
both structured Electronic Health Record (EHR) data and unstructured clinical notes is not …
both structured Electronic Health Record (EHR) data and unstructured clinical notes is not …
Prompting large language models for zero-shot clinical prediction with structured longitudinal electronic health record data
The inherent complexity of structured longitudinal Electronic Health Records (EHR) data
poses a significant challenge when integrated with Large Language Models (LLMs), which …
poses a significant challenge when integrated with Large Language Models (LLMs), which …
EMERGE: Enhancing Multimodal Electronic Health Records Predictive Modeling with Retrieval-Augmented Generation
The integration of multimodal Electronic Health Records (EHR) data has significantly
advanced clinical predictive capabilities. Existing models, which utilize clinical notes and …
advanced clinical predictive capabilities. Existing models, which utilize clinical notes and …
ColaCare: Enhancing Electronic Health Record Modeling through Large Language Model-Driven Multi-Agent Collaboration
We introduce ColaCare, a framework that enhances Electronic Health Record (EHR)
modeling through multi-agent collaboration driven by Large Language Models (LLMs). Our …
modeling through multi-agent collaboration driven by Large Language Models (LLMs). Our …
Learnable Prompt as Pseudo-Imputation: Reassessing the Necessity of Traditional EHR Data Imputation in Downstream Clinical Prediction
Analyzing the health status of patients based on Electronic Health Records (EHR) is a
fundamental research problem in medical informatics. The presence of extensive missing …
fundamental research problem in medical informatics. The presence of extensive missing …
MediTab: Scaling Medical Tabular Data Predictors via Data Consolidation, Enrichment, and Refinement
Tabular data prediction has been employed in medical applications such as patient health
risk prediction. However, existing methods usually revolve around the algorithm design …
risk prediction. However, existing methods usually revolve around the algorithm design …
TransLSTD: Augmenting hierarchical disease risk prediction model with time and context awareness via disease clustering
T You, Q Dang, Q Li, P Zhang, G Wu, W Huang - Information Systems, 2024 - Elsevier
The use of electronic health records has become widespread, providing a valuable source
of information for predicting disease risk. While deep neural network models have been …
of information for predicting disease risk. While deep neural network models have been …
Leveraging Prototype Patient Representations with Feature-Missing-Aware Calibration to Mitigate EHR Data Sparsity
Electronic Health Record (EHR) data frequently exhibits sparse characteristics, posing
challenges for predictive modeling. Current direct imputation such as matrix imputation …
challenges for predictive modeling. Current direct imputation such as matrix imputation …
PRISM: Mitigating EHR Data Sparsity via Learning from Missing Feature Calibrated Prototype Patient Representations
Electronic Health Records (EHRs) provide valuable patient data but often suffer from
sparsity issue, posing significant challenges in predictive modeling. Conventional imputation …
sparsity issue, posing significant challenges in predictive modeling. Conventional imputation …