Treatment planning for chronic diseases is a critical task in medical artificial intelligence, particularly in traditional Chinese medicine (TCM). However, generating optimized sequential treatment strategies for patients with chronic diseases in different clinical encounters remains a challenging issue that requires further exploration. In this study, we proposed a TCM herbal prescription planning framework based on deep reinforcement learning for chronic disease treatment (PrescDRL). PrescDRL is a sequential herbal prescription optimization model that focuses on long-term effectiveness rather than achieving maximum reward at every step, thereby ensuring better patient outcomes. We constructed a high-quality benchmark dataset for sequential diagnosis and treatment of diabetes and evaluated PrescDRL against this benchmark. Our results showed that PrescDRL achieved a higher curative effect, with the single-step reward improving by 117% and 153% compared to doctors. Furthermore, PrescDRL outperformed the benchmark in prescription prediction, with precision improving by 40.5% and recall improving by 63%. Overall, our study demonstrates the potential of using artificial intelligence to improve clinical intelligent diagnosis and treatment in TCM.

翻译：慢性疾病治疗规划是医学人工智能中的关键任务，尤其在中医领域。然而，如何为慢性病患者在不同临床就诊中生成优化的序贯治疗策略，仍是一个需要进一步探索的挑战性问题。本研究提出了一种基于深度强化学习的中医草药处方规划框架（PrescDRL），用于慢性病治疗。PrescDRL是一种序贯草药处方优化模型，其核心在于关注长期疗效而非每一步的最大化奖励，从而确保更优的患者预后。我们构建了糖尿病序贯诊疗的高质量基准数据集，并在此基准上对PrescDRL进行了评估。结果显示，PrescDRL取得了更高疗效，单步奖励较医生处方提升117%和153%。此外，在处方预测方面，PrescDRL也显著优于基准模型，精确率提升40.5%，召回率提升63%。总体而言，本研究证明了人工智能在提升中医临床智能诊疗方面的潜力。