发表机构
Charles Sturt University; Rensselaer Polytechnic Institute（查尔斯斯特大学; 伦斯勒理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出基于LLM的CKD筛查框架，在零/少样本设置下评估其性能，发现LLM在低数据场景中表现有竞争力但稳定性不足，可作为标注数据有限时的灵活补充方法。
AI 中文摘要
慢性肾病（CKD）的早期筛查对及时干预至关重要，但大多数机器学习（ML）和深度学习（DL）方法都需要标注数据和模型训练，限制了它们在实际筛查场景中的应用。本研究评估了大型语言模型（LLMs）在零样本和少样本上下文学习设置下用于CKD筛查的有效性，并将其与传统ML和DL方法进行了比较。我们提出了一个框架，该框架使用临床选定的表格特征和结构化提示模板，实现无需特定任务训练的基于LLM的推理。我们在多种提示风格、特征配置和数据设置下评估了LLM的性能，并将其与标准ML、DL、表格基础模型（TFM）基线以及现有CKD筛查工具进行了比较。结果表明，LLM仅使用少量示例即可实现有竞争力的性能，在低数据场景中通常与传统方法相当或优于传统方法。然而，它们的性能仍取决于模型，且随着输入复杂性的增加稳定性会降低。相比之下，ML、DL和TFM模型随着训练数据的增加表现出更稳定的提升。总体而言，研究结果凸显了数据效率与稳定性之间的权衡，表明当标注数据有限时，LLMs可作为CKD筛查的灵活补充方法。
英文摘要
Early screening of chronic kidney disease (CKD) is critical for timely intervention, yet most machine learning (ML) and deep learning (DL) approaches require labeled data and model training, limiting their use in real-world screening settings. This study evaluates the effectiveness of large language models (LLMs) for CKD screening under zero-shot and few-shot in-context learning settings and compares them with traditional ML and DL methods. We propose a framework that uses clinically selected tabular features and structured prompt templates to enable LLM-based inference without task-specific training. LLM performance is evaluated across multiple prompt styles, feature configurations, and data settings, and compared with standard ML, DL, and tabular foundation model (TFM) baselines, and existing CKD screening tools. The results show that LLMs can achieve competitive performance using only a small number of examples, often matching or outperforming traditional approaches in low-data settings. However, their performance remains model-dependent and less stable as input complexity increases. In contrast, ML, DL, and TFM models show more consistent improvement with larger training data. Overall, the findings highlight a trade-off between data efficiency and stability, suggesting that LLMs may serve as a flexible complementary approach for CKD screening when labeled data are limited. To facilitate further research and reproducibility, the code has been made publicly available at https://github.com/akabircs/LLM4CKD
CommentsAccepted at ICDM 2026
