AI-Driven Prediction of Cancer Pain Episodes: A Hybrid Decision Support Approach
This provides a tool for proactive pain management in lung cancer patients, though it is incremental as it combines existing methods for a specific clinical application.
The paper tackled predicting cancer pain episodes in lung cancer patients by developing a hybrid machine learning and large language model pipeline using electronic health record data, achieving accuracies of 0.874 at 48 hours and 0.917 at 72 hours with sensitivity improvements of 8.6% and 10.4%.
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.