PrescDRL: deep reinforcement learning for herbal prescription planning in treatment of chronic diseases.

ABSTRACT: 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 evaluate

SUBMITTER: Yang K 

PROVIDER: S-EPMC11481742 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature