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Treatment planning for chronic diseases is a critical task in medicalartificial intelligence, particularly in traditional Chinese medicine (TCM).However, generating optimized sequential treatment strategies for patients withchronic diseases in different clinical encounters remains a challenging issuethat requires further exploration. In this study, we proposed a TCM herbalprescription planning framework based on deep reinforcement learning forchronic disease treatment (PrescDRL). PrescDRL is a sequential herbalprescription optimization model that focuses on long-term effectiveness ratherthan achieving maximum reward at every step, thereby ensuring better patientoutcomes. We constructed a high-quality benchmark dataset for sequentialdiagnosis and treatment of diabetes and evaluated PrescDRL against thisbenchmark. 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, ourstudy demonstrates the potential of using artificial intelligence to improveclinical intelligent diagnosis and treatment in TCM.

A optimization framework for herbal prescription planning based on deep reinforcement learning