Cancer treatments are known to introduce cardiotoxicity, negatively impacting outcomes and survivorship. Identifying cancer patients at risk of heart failure (HF) is critical to improving cancer treatment outcomes and safety. This study examined machine learning (ML) models to identify cancer patients at risk of HF using electronic health records (EHRs), including traditional ML, Time-Aware long short-term memory (T-LSTM), and large language models (LLMs) using novel narrative features derived from the structured medical codes. We identified a cancer cohort of 12,806 patients from the University of Florida Health, diagnosed with lung, breast, and colorectal cancers, among which 1,602 individuals developed HF after cancer. The LLM, GatorTron-3.9B, achieved the best F1 scores, outperforming the traditional support vector machines by 39%, the T-LSTM deep learning model by 7%, and a widely used transformer model, BERT, by 5.6%. The analysis shows that the proposed narrative features remarkably increased feature density and improved performance.

使用电子健康记录（EHR）以及传统机器学习、时间感知长短期记忆（T-LSTM）和大型语言模型（LLM）等机器学习模型，通过从结构化医学代码中提取的新颖叙述特征来识别存在心力衰竭（HF）风险的癌症患者，并发现大型语言模型GatorTron-3.9B在性能方面超过了传统支持向量机39％、T-LSTM深度学习模型7％和广泛使用的变换器模型BERT 5.6％，表明所提出的叙述特征显著提高了性能。

叙事特征或结构特征？用大型语言模型识别存在心力衰竭风险的癌症患者的研究