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Updated: Jan 30, 2026

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跨领域的神经协作过用于个性化的草药处方建议
Xin Dong1, Wansong Zhang1, Kuo Yang2
1Department of Artificial Intelligence, Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, School of Computer Science and Technology, Beijing Jiaotong University, Beijing, 100044, China.
Chinese medicine
|January 29, 2026
概括
这项研究介绍了PresRecCDL,这是一种用于草药处方建议 (HPR) 的新框架,该框架使用跨域神经协作过来解决数据稀疏性. 该模型提高了传统中医 (TCM) 诊断和治疗的准确性和效率.
科学领域:
- 计算医学是一种计算医学.
- 生物信息学是一种生物信息学.
- 传统中国医学 (TCM) 是一种
背景情况:
- 草药处方是TCM不可或缺的组成部分,但有效的草药处方建议 (HPR) 被数据稀缺所阻碍,特别是关于草药-症状关系.
- 现有的HPR模型由于不完整的临床数据而面临挑战,限制了它们的预测准确性和临床实用性.
研究的目的:
- 开发一个新的草药处方建议框架,PresRecCDL,以克服数据稀疏性和改进HPR.
- 为了利用跨领域的学习和神经协作过来增强草药-症状相互作用的建模.
主要方法:
- 引入了PresRecCDL,这是一个整合跨领域神经协作过的框架,用于草药处方建议.
- 采用跨领域学习机制,将草药和症状特征映射到一个统一的空间中,减少噪音和数据稀疏性.
- 利用神经协作过来生成个性化的草药处方建议.
主要成果:
- 与最先进的 (SOTA) 模型相比,PresRecCDL在综合性实验中表现出更高的性能.
- 废弃性研究证实了PresRecCDL中的单个模块的有效性.
- 超参数调整验证了该模型的稳定性,网络药理学案例研究强调了其科学严谨性和分子水平的可行性.
结论:
- PresRecCDL模型显著提高了草药处方推系统的性能.
- 这一进步有助于提高中国传统医学的临床治疗效率和精度.
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