Su Chong, Han Peijin, Jiang Bingxu, Liu Cunzhi, Chen Jie
College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.
School of Medicine, Johns Hopkins University, Baltimore, MA 21224, USA.
Technol Health Care. 2019;27(S1):367-381. doi: 10.3233/THC-199034.
Traditional Chinese Medicine (TCM) multiple-acupoints stimulation is widely used to improve dysphagia among post-stroke patients. However, prior research in evidence-based acupuncture mostly focused on the treatment effects of single acupoint's on dysphagia, while the evidence of optimal sequence of multiple-acupoints stimulation remains limited. In this paper, we developed an evaluation method of hybrid knowledge (deterministic knowledge and the experiential group decision knowledge) sequences based on segmentation mechanism of sub-sequence fragments, and then, we proposed a Monte Carlo Tree Search (MCTS) sequential decision-making method under the hybrid knowledge. Thereafter, we applied this proposed sequential decision-making approach to optimizing sequential decision-making schema of multiple-acupoints stimulation, to treat dysphagia among post-stroke patients. Finally, we verified the validity and the feasibility of this method by comparing it to other sequential decision-making search methods.
中医多穴位刺激被广泛用于改善中风后患者的吞咽困难。然而，以往基于循证针灸的研究大多集中在单个穴位对吞咽困难的治疗效果上，而关于多穴位刺激最佳顺序的证据仍然有限。在本文中，我们基于子序列片段的分割机制，开发了一种混合知识（确定性知识和经验性群体决策知识）序列的评估方法，然后，我们提出了一种混合知识下的蒙特卡罗树搜索（MCTS）序列决策方法。此后，我们将这种提出的序列决策方法应用于优化多穴位刺激的序列决策模式，以治疗中风后患者的吞咽困难。最后，我们通过将该方法与其他序列决策搜索方法进行比较，验证了该方法的有效性和可行性。
