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

arXiv: https://arxiv.org/abs/2304.12828 · HTML: https://mlsanity.com/paper/2304.12828/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 45 references that are in the map.
- First posted on arXiv 25 Apr 2023.

## Builds on (most cited first, 10 of 10)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Deep Recurrent Q-Learning for Partially Observable MDPs](https://mlsanity.com/paper/1507.06527/) (2015)
- [Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation](https://mlsanity.com/paper/1807.01473/) (2018)
- [Deep Reinforcement Learning for Sepsis Treatment](https://mlsanity.com/paper/1711.09602/) (2017)
- [Continuous State-Space Models for Optimal Sepsis Treatment - a Deep Reinforcement Learning Approach](https://mlsanity.com/paper/1705.08422/) (2017)
- [Deep Reinforcement Learning for Dynamic Treatment Regimes on Medical Registry Data](https://mlsanity.com/paper/1801.09271/) (2018)
- [Model-Based Reinforcement Learning for Sepsis Treatment](https://mlsanity.com/paper/1811.09602/) (2018)
- [Deep Reinforcement Learning for Optimal Critical Care Pain Management with Morphine using Dueling Double-Deep Q Networks](https://mlsanity.com/paper/1904.11115/) (2019)
- [Automatic construction of Chinese herbal prescription from tongue image via CNNs and auxiliary latent therapy topics](https://mlsanity.com/paper/1802.02203/) (2018)
- [Convolutional herbal prescription building method from multi-scale facial features](https://mlsanity.com/paper/1812.06847/) (2018)

## Built on by (most cited first, 1 of 1)

- [Beyond Prediction: Reinforcement Learning as the Defining Leap in Healthcare AI](https://mlsanity.com/paper/2508.21101/) (2025)

## Abstract (arXiv)

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.