Institute of Medical Intelligence, Department of Artificial Intelligence, Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer Science & Technology, Beijing Jiaotong University, Beijing, 100044, China.
The First Affiliated Hospital, Henan University of Chinese Medicine, Henan, 450000, China.
Phytomedicine. 2024 Dec;135:156116. doi: 10.1016/j.phymed.2024.156116. Epub 2024 Oct 1.
Herbal prescription recommendation (HPR) is a hotspot in the research of clinical intelligent decision support. Recently plentiful HPR models based on deep neural networks have been proposed. Owing to insufficient data, e.g., lack of knowledge of molecular, TCM theory, and herbal dosage in HPR modeling, the existing models suffer from challenges, e.g., plain prediction precision, and are far from real-world clinics.
To address these problems, we proposed a novel herbal prescription recommendation model with the representation fusion of large TCM semantics and molecular knowledge (termed PresRecRF).
PresRecRF comprises three key modules. The representation learning module consists of two key components: a molecular knowledge representation component, integrating molecular knowledge into the herb-symptom-protein knowledge graph to enhance representations for herbs and symptoms; and a TCM knowledge representation component, leveraging BERT and ChatGPT to acquire TCM knowledge-enriched semantic representations. We introduced a representation fusion module to effectively merge molecular and TCM semantic representations. In the herb recommendation module, a multi-task objective loss is implemented to predict both herbs and dosages simultaneously.
The experimental results on two clinical datasets show that PresRecRF can achieve the optimal performance. Further analysis of ablation, hyper-parameters, and case studies indicate the effectiveness and reliability of the proposed model, suggesting that it can help precision medicine and treatment recommendations.
The entire process of the proposed PresRecRF model closely mirrors the actual diagnosis and treatment procedures carried out by doctors, which are better applied in real clinical scenarios. The source codes of PresRecRF is available at https://github.com/2020MEAI/PresRecRF.
草药处方推荐（HPR）是临床智能决策支持研究的热点。最近，基于深度学习神经网络的大量 HPR 模型已经被提出。由于数据不足，例如在 HPR 建模中缺乏分子、中医理论和草药剂量方面的知识，现有的模型面临着挑战，例如预测精度不高，远远不能满足实际临床需求。
为了解决这些问题，我们提出了一种新的草药处方推荐模型，该模型融合了大量中医语义和分子知识的表示（称为 PresRecRF）。
PresRecRF 包括三个关键模块。表示学习模块由两个关键组件组成：分子知识表示组件，将分子知识集成到草药-症状-蛋白质知识图中，增强草药和症状的表示；以及中医知识表示组件，利用 BERT 和 ChatGPT 来获取富含中医知识的语义表示。我们引入了一个表示融合模块，以有效地融合分子和中医语义表示。在草药推荐模块中，采用多任务目标损失函数来同时预测草药和剂量。
在两个临床数据集上的实验结果表明，PresRecRF 可以实现最优性能。消融、超参数和案例研究的进一步分析表明了所提出模型的有效性和可靠性，表明它可以帮助实现精准医学和治疗推荐。
所提出的 PresRecRF 模型的整个过程紧密模拟了医生实际的诊断和治疗过程，更适用于实际的临床场景。PresRecRF 的源代码可在 https://github.com/2020MEAI/PresRecRF 上获取。
