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20162025
most citedAn Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processing

10 citations · 22 across the 15 of their papers we have counts for

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cs.CL2024

RAG-RLRC-LaySum at BioLaySumm: Integrating Retrieval-Augmented Generation and Readability Control for Layman Summarization of Biomedical Texts

Yuelyu Ji, Zhuochun Li, Rui Meng +7

This paper introduces the RAG-RLRC-LaySum framework, designed to make complex biomedical research understandable to laymen through advanced Natural Language Processing (NLP) techni…

cs.CL20241 cited

PRISM: Patient Records Interpretation for Semantic Clinical Trial Matching using Large Language Models

Shashi Kant Gupta, Aditya Basu, Mauro Nievas +10

Clinical trial matching is the task of identifying trials for which patients may be potentially eligible. Typically, this task is labor-intensive and requires detailed verification…

cs.CL20243 cited

Assertion Detection Large Language Model In-context Learning LoRA Fine-tuning

Yuelyu Ji, Zeshui Yu, Yanshan Wang

In this study, we aim to address the task of assertion detection when extracting medical concepts from clinical notes, a key process in clinical natural language processing (NLP).…

cs.CL20242 cited

Enhancing Large Language Models for Clinical Decision Support by Incorporating Clinical Practice Guidelines

David Oniani, Xizhi Wu, Shyam Visweswaran +4

Background Large Language Models (LLMs), enhanced with Clinical Practice Guidelines (CPGs), can significantly improve Clinical Decision Support (CDS). However, methods for incorpor…

cs.CL202310 cited

An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processing

Sonish Sivarajkumar, Mark Kelley, Alyssa Samolyk-Mazzanti +2

Large language models (LLMs) have shown remarkable capabilities in Natural Language Processing (NLP), especially in domains where labeled data is scarce or expensive, such as clini…

cs.CL2023

Less Likely Brainstorming: Using Language Models to Generate Alternative Hypotheses

Liyan Tang, Yifan Peng, Yanshan Wang +3

A human decision-maker benefits the most from an AI assistant that corrects for their biases. For problems such as generating interpretation of a radiology report given findings, a…