# AI-Driven Prediction of Cancer Pain Episodes: A Hybrid Decision Support Approach

arXiv: https://arxiv.org/abs/2512.16739 · HTML: https://mlsanity.com/paper/2512.16739/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 37 references that are in the map.
- First posted on arXiv 18 Dec 2025.

## Builds on (most cited first, 2 of 2)

- [Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study](https://mlsanity.com/paper/2305.13062/) (2023)
- [Small Models are LLM Knowledge Triggers on Medical Tabular Prediction](https://mlsanity.com/paper/2403.01570/) (2024)

## Built on by (most cited first, 1 of 1)

- [PLeDO: Pain Level Detection for Osteoarthritis from EMR Data](https://mlsanity.com/paper/2608.15719/) (2026)

## Abstract (arXiv)

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.