通过大语言模型对大规模非结构化电子健康记录进行结构化处理以改进放疗后的死亡率预测
Improving Mortality Prediction After Radiotherapy with Large Language Model Structuring of Large-Scale Unstructured Electronic Health Records
AI总结：
本研究提出RT-Surv框架，整合开源大语言模型将非结构化电子健康记录结构化并结合临床数据，在3万余患者及外部队列验证中显著提升放疗后死亡率预测的一致性指数与风险分层效果，增强模型可解释性。
摘要：
放疗（RT）中的准确生存预测对于优化治疗决策至关重要。本研究开发并验证了RT-Surv框架，该框架整合了通用领域的开源大语言模型（LLMs），将非结构化电子健康记录与结构化临床数据相结合进行结构化处理。研究使用34276名患者的数据以及包含852名患者的外部队列，该框架成功将非结构化临床信息转化为结构化格式。在外部验证中，纳入LLM结构化的临床特征将一致性指数从0.779提升至0.842，表现出显著的性能提升。由LLM提取的关键结构化特征，如疾病范围、整体状况和放疗目的，显示出很高的预测重要性，且与通过传统统计分析确定的具有统计学意义的预测因子高度吻合，从而提升了模型的可解释性。此外，该框架增强了风险分层能力，利用LLM结构化的临床特征能够更清晰地区分低、中、高危组（p < 0.001）。这些发现凸显了LLM将非结构化数据转化为可操作洞见的潜力，可改进临床中的预测建模和患者预后。
英文摘要：
Accurate survival prediction in radiotherapy (RT) is critical for optimizing treatment decisions. This study developed and validated the RT-Surv framework, which integrates general-domain, open-source large language models (LLMs) to structure unstructured electronic health records alongside structured clinical data. Using data from 34,276 patients and an external cohort of 852, the framework successfully transformed unstructured clinical information into structured formats. Incorporating LLM-structured clinical features improved the concordance index from 0.779 to 0.842 during external validation, demonstrating a significant performance enhancement. Key LLM-structured features, such as disease extent, general condition, and RT purpose, showed high predictive importance and aligned closely with statistically significant predictors identified through conventional statistical analyses, thereby improving model interpretability. Furthermore, the framework enhanced risk stratification, enabling more distinct differentiation among low-, intermediate-, and high-risk groups (p < 0.001) using LLM-structured clinical features. These findings highlight the potential of LLMs to convert unstructured data into actionable insights, improving predictive modeling and patient outcomes in clinics.
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