用大模型和强化学习为癌症患者定制治疗方案，提升推荐效果。
Prior-informed optimization of treatment recommendation via bandit algorithms trained on large language model-processed historical records
- 用大模型处理病历文本，生成结构化数据，准确率达93.2%
- 通过合成数据与真实数据对比验证，生成数据准确率55%
- 在结肠癌数据上实现0.60-0.61的平均奖励，优于现有方法
当前医疗实践依赖标准化框架与经验方法，忽视个体差异，导致治疗效果不佳。本研究构建了一个整合大语言模型（LLMs）、条件表格式生成对抗网络（CTGAN）、T-learner反事实模型与上下文相关贝叶斯算法的综合系统，提供数据驱动的个性化临床推荐。该方法利用大模型将非结构化病历文本转化为结构化数据（准确率93.2%），采用CTGAN生成真实感患者数据（经双样本检验准确率55%），部署T-learner预测个体化治疗响应（准确率84.3%），并结合先验信息的上下文贝叶斯算法，在探索新疗法与利用已有知识间取得平衡。在三期结肠癌数据集上测试表明，其提出的KernelUCB方法在5000轮中获得0.60-0.61的平均奖励，显著优于参考方法。该系统克服了在线学习中的冷启动问题，提升了计算效率，推动了针对特定患者特征的精准医疗发展。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。