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

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 i...

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Bibliographic Details
Published in:arXiv.org 2023-04
Main Authors: Kuo, Yang, Yu, Zecong, Su, Xin, He, Xiong, Wang, Ning, Zheng, Qiguang, Yu, Feidie, Liu, Zhuang, Wen, Tiancai, Zhou, Xuezhong
Format: Article
Language:English
Subjects:
Artificial intelligence
Benchmarks
Chinese medicine
Chronic illnesses
Deep learning
Diagnosis
Health services
Medical diagnosis
Optimization models
Traditional Chinese medicine
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Summary: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.
ISSN:2331-8422