Cover Image

EHRAgent: Code Empowers Large Language Models for Few-shot Complex Tabular Reasoning on Electronic Health Records

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Shi, Wenqi, Xu, Ran, Zhuang, Yuchen, Yu, Yue, Zhang, Jieyu, Wu, Hang, Zhu, Yuanda, Ho, Joyce, Yang, Carl, Wang, May D.
Format:	Preprint
Published:	2024
Subjects:	Computation and Language Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

RAM-EHR: Retrieval Augmentation Meets Clinical Predictions on Electronic Health Records
by: Xu, Ran, et al.
Published: (2024)

MedAdapter: Efficient Test-Time Adaptation of Large Language Models towards Medical Reasoning
by: Shi, Wenqi, et al.
Published: (2024)

Collab-RAG: Boosting Retrieval-Augmented Generation for Complex Question Answering via White-Box and Black-Box LLM Collaboration
by: Xu, Ran, et al.
Published: (2025)

BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers
by: Xu, Ran, et al.
Published: (2024)

Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models
by: Xu, Ran, et al.
Published: (2023)

AceSearcher: Bootstrapping Reasoning and Search for LLMs via Reinforced Self-Play
by: Xu, Ran, et al.
Published: (2025)

RAG in the Wild: On the (In)effectiveness of LLMs with Mixture-of-Knowledge Retrieval Augmentation
by: Xu, Ran, et al.
Published: (2025)

MedAgentGym: A Scalable Agentic Training Environment for Code-Centric Reasoning in Biomedical Data Science
by: Xu, Ran, et al.
Published: (2025)

LLMs-based Few-Shot Disease Predictions using EHR: A Novel Approach Combining Predictive Agent Reasoning and Critical Agent Instruction
by: Cui, Hejie, et al.
Published: (2024)

Summarize-Exemplify-Reflect: Data-driven Insight Distillation Empowers LLMs for Few-shot Tabular Classification
by: Yuan, Yifei, et al.
Published: (2025)

Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning
by: Shi, Ruxue, et al.
Published: (2025)

Zero-shot and Few-shot Generation Strategies for Artificial Clinical Records
by: Frayling, Erlend, et al.
Published: (2024)

FORESTLLM: Large Language Models Make Random Forest Great on Few-shot Tabular Learning
by: Yang, Zhihan, et al.
Published: (2026)

Multimodal Fusion of EHR in Structures and Semantics: Integrating Clinical Records and Notes with Hypergraph and LLM
by: Cui, Hejie, et al.
Published: (2024)

Prompt Space Optimizing Few-shot Reasoning Success with Large Language Models
by: Shi, Fobo, et al.
Published: (2023)

Extracting OPQRST in Electronic Health Records using Large Language Models with Reasoning
by: Luo, Zhimeng, et al.
Published: (2025)

How Many Validation Labels Do You Need? Exploring the Design Space of Label-Efficient Model Ranking
by: Hu, Zhengyu, et al.
Published: (2023)

Few-shot Vision-based Human Activity Recognition with MLLM-based Visual Reinforcement Learning
by: Zheng, Wenqi, et al.
Published: (2025)

LLM Empowered Prototype Learning for Zero and Few-Shot Tasks on Tabular Data
by: Wang, Peng, et al.
Published: (2025)

A Generative Approach for Semantic Auditing of Electronic Health Records
by: Girshovitz, Irena, et al.
Published: (2025)

WorkForceAgent-R1: Incentivizing Reasoning Capability in LLM-based Web Agents via Reinforcement Learning
by: Zhuang, Yuchen, et al.
Published: (2025)

BoostLLM: Boosting-inspired LLM Fine-tuning for Few-shot Tabular Classification
by: Wang, Yi-Siang, et al.
Published: (2026)

Envisioning Class Entity Reasoning by Large Language Models for Few-shot Learning
by: Liu, Mushui, et al.
Published: (2024)

FoMo-0D: A Foundation Model for Zero-shot Tabular Outlier Detection
by: Shen, Yuchen, et al.
Published: (2024)

GraphWalker: Graph-Guided In-Context Learning for Clinical Reasoning on Electronic Health Records
by: Fang, Yue, et al.
Published: (2026)

CtlGAN: Few-shot Artistic Portraits Generation with Contrastive Transfer Learning
by: Wang, Yue, et al.
Published: (2022)

Explainable Few-shot Knowledge Tracing
by: Li, Haoxuan, et al.
Published: (2024)

Retrieval-aligned Tabular Foundation Models Enable Robust Clinical Risk Prediction in Electronic Health Records Under Real-world Constraints
by: Pham, Minh-Khoi, et al.
Published: (2026)

Minimizing Racial Algorithmic Bias when Predicting Electronic Health Record Data Completeness
by: Priyanka Anand, et al.
Published: (2025)

Learning to Learn for Few-shot Continual Active Learning
by: Ho, Stella, et al.
Published: (2023)

Large Language Models are Powerful Electronic Health Record Encoders
by: Hegselmann, Stefan, et al.
Published: (2025)

Large Language Models to Identify Social Determinants of Health in Electronic Health Records
by: Guevara, Marco, et al.
Published: (2023)

Evaluating LLM Abilities to Understand Tabular Electronic Health Records: A Comprehensive Study of Patient Data Extraction and Retrieval
by: Lovon, Jesus, et al.
Published: (2025)

Generating Querying Code from Text for Multi-Modal Electronic Health Record
by: ZHang, Mengliang
Published: (2025)

See it, Think it, Sorted: Large Multimodal Models are Few-shot Time Series Anomaly Analyzers
by: Zhuang, Jiaxin, et al.
Published: (2024)

CRISP: Compressed Reasoning via Iterative Self-Policy Distillation
by: Sang, Hejian, et al.
Published: (2026)

Generative Foundation Model for Structured and Unstructured Electronic Health Records
by: Sivarajkumar, Sonish, et al.
Published: (2025)

SARF: Aliasing Relation Assisted Self-Supervised Learning for Few-shot Relation Reasoning
by: Meng, Lingyuan, et al.
Published: (2023)

Zero-shot Medical Event Prediction Using a Generative Pre-trained Transformer on Electronic Health Records
by: Redekop, Ekaterina, et al.
Published: (2025)

Scaling Agentic Reinforcement Learning for Tool-Integrated Reasoning in VLMs
by: Lu, Meng, et al.
Published: (2025)