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

TL;DR

This paper presents a novel system combining LLMs, generative models, counterfactual analysis, and bandit algorithms to improve personalized treatment recommendations, demonstrating superior performance on cancer treatment data.

Contribution

It introduces an integrated framework that processes unstructured medical data, generates synthetic patient data, predicts treatment responses, and applies prior-informed bandits for personalized therapy selection.

Findings

KernelUCB achieved 0.60-0.61 reward scores in experiments.

System effectively overcomes cold-start issues in online learning.

Enhanced personalized treatment recommendations demonstrated on cancer datasets.

Abstract

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.