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. Motivated by the urgent need for swift decision-making during new
disease outbreaks, where traditional predictive models often fail due to a lack
of historical data, this research investigates the adaptability of LLMs, like
GPT-4, to EHR data. We particularly focus on their zero-shot capabilities,
which enable them to make predictions in scenarios in which they haven't been
explicitly trained. In response to the longitudinal, sparse, and
knowledge-infused nature of EHR data, our prompting approach involves taking
into account specific EHR characteristics such as units and reference ranges,
and employing an in-context learning strategy that aligns with clinical
contexts. Our comprehensive experiments on the MIMIC-IV and TJH datasets
demonstrate that with our elaborately designed prompting framework, LLMs can
improve prediction performance in key tasks such as mortality, length-of-stay,
and 30-day readmission by about 35\%, surpassing ML models in few-shot
settings. Our research underscores the potential of LLMs in enhancing clinical
decision-making, especially in urgent healthcare situations like the outbreak
of emerging diseases with no labeled data. The code is publicly available at
this https URL for reproducibility.

针对结构化长期电子健康记录 (EHR) 数据与大型语言模型 (LLMs) 集成时的固有复杂性，本研究调查了像 GPT-4 这样的 LLMs 对 EHR 数据的适应性。特别关注其零样本能力，使其能够在并未明确训练的情况下进行预测。通过考虑特定的 EHR 特征 (如单位和参考范围)，采用与临床环境相一致的上下文学习策略，我们的寻求方法能够应对 EHR 数据的纵向、稀疏和知识注入的特性。通过对 MIMIC-IV 和 TJH 数据集进行综合实验，证明了我们精心设计的提示框架下，LLMs 在关键任务 (如死亡率、住院天数和 30 天再入院率) 的预测性能可以提高约 35％，在少样本情景中超越了机器学习模型。我们的研究强调了 LLMs 在提高临床决策能力方面的潜力，尤其是在没有标签数据的紧急医疗情况下，如新发疾病的爆发。可通过此 https URL 获得代码，以便能重现研究结果。