用混合模型提前48-72小时预测肺癌患者疼痛发作，提升干预及时性。
AI-Driven Prediction of Cancer Pain Episodes: A Hybrid Decision Support Approach
- 融合机器学习与大语言模型，分析结构化和非结构化病历数据。
- 48小时预测准确率87.6%，72小时达91.7%，敏感性提升超10%。
- 适合临床医生用于癌痛早期预警，提升治疗精准度与资源效率。
肺癌患者常出现爆发性疼痛，高达91%需及时干预。为实现主动疼痛管理，我们提出一种混合机器学习与大语言模型的决策支持流程，利用结构化与非结构化电子病历数据，在住院后48小时和72小时内预测疼痛发作。基于266名住院患者的回顾性队列，特征包括人口统计、肿瘤分期、生命体征及WHO分级镇痛药使用情况。机器学习模块捕捉药物使用的时间趋势，大语言模型解析模糊用药记录与自由文本临床笔记。多模态融合提升了模型敏感性与可解释性。该框架在48小时预测中准确率达0.876，72小时达0.917，敏感性分别提升10.6%和10.7%，归因于大语言模型的增强作用。该混合方法提供可临床解读且可扩展的早期疼痛预测工具，有望提升肿瘤治疗精准度并优化资源配置。
原文摘要 · Abstract (English)
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 0.876 (48h) and 0.917 (72h), with improvements in sensitivity of 10.6% and 10.7%, respectively, attributable to large language model augmentation. 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.
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