{"ID":2827638,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.16739","arxiv_id":"2512.16739","title":"AI-Driven Prediction of Cancer Pain Episodes: A Hybrid Decision Support Approach","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2512.16739","url_pdf":"https://arxiv.org/pdf/2512.16739v2","authors":"[\"Yipeng Zhuang\",\"Yifeng Guo\",\"Yuewen Li\",\"Yuheng Wu\",\"Philip Leung-Ho Yu\",\"Tingting Song\",\"Zhiyong Wang\",\"Kunzhong Zhou\",\"Weifang Wang\",\"Li Zhuang\"]","published":"2025-12-18T16:37:29Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[\"Language Model\"]","has_code":false}