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20232025
most citedTACCO: Task-guided Co-clustering of Clinical Concepts and Patient Visits for Disease Subtyping based on EHR Data

5 citations · 20 across the 16 of their papers we have counts for

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Showing 2024Show all

9 papers · 1 filter

cs.CL2024★ 1 cited

SimRAG: Self-Improving Retrieval-Augmented Generation for Adapting Large Language Models to Specialized Domains

Ran Xu, Hui Liu, Sreyashi Nag +8

Retrieval-augmented generation (RAG) enhances the question-answering (QA) abilities of large language models (LLMs) by integrating external knowledge. However, adapting general-pur…

cs.CL2024

Boosting Reward Model with Preference-Conditional Multi-Aspect Synthetic Data Generation

Jiaming Shen, Ran Xu, Yennie Jun +6

Reward models (RMs) are crucial for aligning large language models (LLMs) with human preferences. They are trained using preference datasets where each example consists of one inpu…

cs.LG2024★ 5 cited

TACCO: Task-guided Co-clustering of Clinical Concepts and Patient Visits for Disease Subtyping based on EHR Data

Ziyang Zhang, Hejie Cui, Ran Xu +3

The growing availability of well-organized Electronic Health Records (EHR) data has enabled the development of various machine learning models towards disease risk prediction. Howe…

cs.LG2024★ 1 cited

From Basic to Extra Features: Hypergraph Transformer Pretrain-then-Finetuning for Balanced Clinical Predictions on EHR

Ran Xu, Yiwen Lu, Chang Liu +5

Electronic Health Records (EHRs) contain rich patient information and are crucial for clinical research and practice. In recent years, deep learning models have been applied to EHR…

cs.CL2024

MedAdapter: Efficient Test-Time Adaptation of Large Language Models towards Medical Reasoning

Wenqi Shi, Ran Xu, Yuchen Zhuang +5

Despite their improved capabilities in generation and reasoning, adapting large language models (LLMs) to the biomedical domain remains challenging due to their immense size and co…

cs.CL2024★ 2 cited

BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers

Ran Xu, Wenqi Shi, Yue Yu +6

Developing effective biomedical retrieval models is important for excelling at knowledge-intensive biomedical tasks but still challenging due to the deficiency of sufficient public…

cs.CL2024★ 1 cited

RAM-EHR: Retrieval Augmentation Meets Clinical Predictions on Electronic Health Records

Ran Xu, Wenqi Shi, Yue Yu +5

We present RAM-EHR, a Retrieval AugMentation pipeline to improve clinical predictions on Electronic Health Records (EHRs). RAM-EHR first collects multiple knowledge sources, conver…

cs.LG2024★ 3 cited

Multimodal Fusion of EHR in Structures and Semantics: Integrating Clinical Records and Notes with Hypergraph and LLM

Hejie Cui, Xinyu Fang, Ran Xu +3

Electronic Health Records (EHRs) have become increasingly popular to support clinical decision-making and healthcare in recent decades. EHRs usually contain heterogeneous informati…

cs.CL2024★ 2 cited

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

Wenqi Shi, Ran Xu, Yuchen Zhuang +7

Large language models (LLMs) have demonstrated exceptional capabilities in planning and tool utilization as autonomous agents, but few have been developed for medical problem-solvi…