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.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2512.16739 [cs.AI] |
| (or arXiv:2512.16739v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2512.16739 arXiv-issued DOI via DataCite |
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| Related DOI: | https://doi.org/10.1109/JBHI.2026.3694585
DOI(s) linking to related resources |
From: Yipeng Zhuang [view email]
[v1]
Thu, 18 Dec 2025 16:37:29 UTC (758 KB)
[v2]
Thu, 21 May 2026 03:35:21 UTC (344 KB)
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