Lung cancer patients frequently experience breakthrough pain episodes, with up to 91% requiring timely intervention. To enable proactive pain management, we propose a hybrid machine learning and large language model pipeline that predicts pain episodes within 48 and 72 hours of hospitalization using both structured and unstructured electronic health record data. A retrospective cohort of 266 inpatients was analyzed, with features including demographics, tumor stage, vital signs, and WHO-tiered analgesic use. The machine learning module captured temporal medication trends, while the large language model interpreted ambiguous dosing records and free-text clinical notes. Integrating these modalities improved sensitivity and interpretability. Our framework achieved an accuracy of 0.874 (48h) and 0.917 (72h), with an improvement in sensitivity of 8.6% and 10.4% due to the augmentation of large language model. This hybrid approach offers a clinically interpretable and scalable tool for early pain episode forecasting, with potential to enhance treatment precision and optimize resource allocation in oncology care.

翻译：肺癌患者常经历爆发性疼痛发作，高达91%的患者需要及时干预。为实现主动疼痛管理，我们提出了一种混合机器学习与大型语言模型的流程，利用结构化和非结构化的电子健康记录数据，预测住院后48小时和72小时内的疼痛发作。研究分析了一个包含266名住院患者的回顾性队列，特征包括人口统计学、肿瘤分期、生命体征以及世界卫生组织分级的镇痛药物使用情况。机器学习模块捕捉了药物使用的时间趋势，而大型语言模型则解读了模糊的给药记录和自由文本临床笔记。整合这两种模式提高了敏感性和可解释性。我们的框架在48小时和72小时预测中分别达到了0.874和0.917的准确率，由于大型语言模型的增强，敏感性分别提升了8.6%和10.4%。这种混合方法为早期疼痛发作预测提供了一种临床可解释且可扩展的工具，有望提高肿瘤治疗精度并优化资源分配。