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

TL;DR

This paper introduces PrescDRL, a deep reinforcement learning framework for optimizing herbal prescriptions in traditional Chinese medicine, improving treatment effectiveness and prescription accuracy for chronic diseases.

Contribution

The study presents a novel deep reinforcement learning model tailored for sequential herbal prescription planning in TCM, with a high-quality benchmark dataset and significant performance improvements.

Findings

PrescDRL increased single-step reward by over 117%.

PrescDRL improved prescription precision by 40.5%.

PrescDRL outperformed doctors in treatment effectiveness.

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 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.