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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.16739 |
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| _version_ | 1866918515973292032 |
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| author | Zhuang, Yipeng Guo, Yifeng Li, Yuewen Wu, Yuheng Yu, Philip Leung-Ho Song, Tingting Wang, Zhiyong Zhou, Kunzhong Wang, Weifang Zhuang, Li |
| author_facet | Zhuang, Yipeng Guo, Yifeng Li, Yuewen Wu, Yuheng Yu, Philip Leung-Ho Song, Tingting Wang, Zhiyong Zhou, Kunzhong Wang, Weifang Zhuang, Li |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_16739 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI-Driven Prediction of Cancer Pain Episodes: A Hybrid Decision Support Approach Zhuang, Yipeng Guo, Yifeng Li, Yuewen Wu, Yuheng Yu, Philip Leung-Ho Song, Tingting Wang, Zhiyong Zhou, Kunzhong Wang, Weifang Zhuang, Li Artificial Intelligence 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. |
| title | AI-Driven Prediction of Cancer Pain Episodes: A Hybrid Decision Support Approach |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2512.16739 |