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Found 10 papers, 6 papers with code
AGFSync: Leveraging AI-Generated Feedback for Preference Optimization in Text-to-Image Generation
Text-to-Image (T2I) diffusion models have achieved remarkable success in image generation.
LightM-UNet: Mamba Assists in Lightweight UNet for Medical Image Segmentation
This highlights the potential of Mamba in facilitating model lightweighting.
REALM: RAG-Driven Enhancement of Multimodal Electronic Health Records Analysis via Large Language Models
Leveraging clinical notes and multivariate time-series EHR, existing models often lack the medical context relevent to clinical tasks, prompting the incorporation of external knowledge, particularly from the knowledge graph (KG).
Learnable Prompt as Pseudo-Imputation: Reassessing the Necessity of Traditional EHR Data Imputation in Downstream Clinical Prediction
PAI no longer introduces any imputed data but constructs a learnable prompt to model the implicit preferences of the downstream model for missing values, resulting in a significant performance improvement for all EHR analysis models.
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 are traditionally tailored for natural language processing.
Domain-invariant Clinical Representation Learning by Bridging Data Distribution Shift across EMR Datasets
Due to the limited information about emerging diseases, symptoms are hard to be noticed and recognized, so that the window for clinical intervention could be ignored.
PRISM: Leveraging Prototype Patient Representations with Feature-Missing-Aware Calibration for EHR Data Sparsity Mitigation
Electronic Health Record (EHR) data, while rich in information, often suffers from sparsity, posing significant challenges in predictive modeling.
M$^3$Fair: Mitigating Bias in Healthcare Data through Multi-Level and Multi-Sensitive-Attribute Reweighting Method
In healthcare AI, these attributes can play a significant role in determining the quality of care that individuals receive.
M$^3$Care: Learning with Missing Modalities in Multimodal Healthcare Data
M3Care is an end-to-end model compensating the missing information of the patients with missing modalities to perform clinical analysis.
A Comprehensive Benchmark for COVID-19 Predictive Modeling Using Electronic Health Records in Intensive Care
Many deep learning models have been proposed to conduct clinical predictive tasks such as mortality prediction for COVID-19 patients in intensive care units using Electronic Health Record (EHR) data.
