# Prior-informed optimization of treatment recommendation via bandit algorithms trained on large language model-processed historical records

arXiv: https://arxiv.org/abs/2510.19014 · HTML: https://mlsanity.com/paper/2510.19014/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 35 references that are in the map.
- First posted on arXiv 21 Oct 2025.

## Builds on (most cited first, 8 of 8)

- [Multi-armed Bandit Models for the Optimal Design of Clinical Trials: Benefits and Challenges](https://mlsanity.com/paper/1507.08025/) (2015)
- [A Contextual-bandit-based Approach for Informed Decision-making in Clinical Trials](https://mlsanity.com/paper/1809.00258/) (2018)
- [Jump Starting Bandits with LLM-Generated Prior Knowledge](https://mlsanity.com/paper/2406.19317/) (2024)
- [Leveraging Large Language Models for Knowledge-free Weak Supervision in Clinical Natural Language Processing](https://mlsanity.com/paper/2406.06723/) (2024)
- [The Foundational Capabilities of Large Language Models in Predicting Postoperative Risks Using Clinical Notes](https://mlsanity.com/paper/2402.17493/) (2024)
- [Injecting Prior Knowledge for Transfer Learning into Reinforcement Learning Algorithms using Logic Tensor Networks](https://mlsanity.com/paper/1906.06576/) (2019)
- [Adaptive Experiments Toward Learning Treatment Effect Heterogeneity](https://mlsanity.com/paper/2312.06883/) (2023)
- [Adaptive Randomization Methods for Sequential Multiple Assignment Randomized Trials (SMARTs) via Thompson Sampling](https://mlsanity.com/paper/2401.03268/) (2024)

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

- [A Component-Based Survey of Interactions between Large Language Models and Multi-Armed Bandits](https://mlsanity.com/paper/2601.12945/) (2026)

## Abstract (arXiv)

Current medical practice depends on standardized treatment frameworks and empirical methodologies that neglect individual patient variations, leading to suboptimal health outcomes. We develop a comprehensive system integrating Large Language Models (LLMs), Conditional Tabular Generative Adversarial Networks (CTGAN), T-learner counterfactual models, and contextual bandit approaches to provide customized, data-informed clinical recommendations. The approach utilizes LLMs to process unstructured medical narratives into structured datasets (93.2% accuracy), uses CTGANs to produce realistic synthetic patient data (55% accuracy via two-sample verification), deploys T-learners to forecast patient-specific treatment responses (84.3% accuracy), and integrates prior-informed contextual bandits to enhance online therapeutic selection by effectively balancing exploration of new possibilities with exploitation of existing knowledge. Testing on stage III colon cancer datasets revealed that our KernelUCB approach obtained 0.60-0.61 average reward scores across 5,000 rounds, exceeding other reference methods. This comprehensive system overcomes cold-start limitations in online learning environments, improves computational effectiveness, and constitutes notable progress toward individualized medicine adapted to specific patient characteristics.