ArticleBioMed research international2022
TCMPR: TCM Prescription Recommendation Based on Subnetwork Term Mapping and Deep Learning.
Article in BioMed research international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Cross-domain neural collaborative filtering for personalized herbal prescription recommendation.Chinese medicine · 2026Article
- Advancing the modernization of traditional Chinese medicine through artificial intelligence and multimodal data integration.Chinese medicine · 2026Review
- Digital intelligence technology: new quality productivity for precision traditional Chinese medicine.Frontiers in pharmacology · 2025Review
- PRDAGE: a prescription recommendation framework for traditional Chinese medicine based on data augmentation and multi-graph embedding.PeerJ. Computer science · 2025Article
- TCMM: A unified database for traditional Chinese medicine modernization and therapeutic innovations.Computational and structural biotechnology journal · 2024Article
- Lingdan: enhancing encoding of traditional Chinese medicine knowledge for clinical reasoning tasks with large language models.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- PresRecST: a novel herbal prescription recommendation algorithm for real-world patients with integration of syndrome differentiation and treatment planning.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Traditional Chinese medicine diagnostic prediction model for holistic syndrome differentiation based on deep learning.Integrative medicine research · 2024Article
- Integrating artificial intelligence into the modernization of traditional Chinese medicine industry: a review.Frontiers in pharmacology · 2024Review
- Plant disease prescription recommendation based on electronic medical records and sentence embedding retrieval.Plant methods · 2023Article
- Machine learning in TCM with natural products and molecules: current status and future perspectives.Chinese medicine · 2023Review
- Bioinformatics-based and multiscale convolutional neural network screening of herbal medicines for improving the prognosis of liver cancer: a novel approach.Frontiers in medicine · 2023Article
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Authors and funding
10 authors.
Funding
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Abstract
Traditional Chinese medicine (TCM) has played an indispensable role in clinical diagnosis and treatment. Based on a patient's symptom phenotypes, computation-based prescription recommendation methods can recommend personalized TCM prescription using machine learning and artificial intelligence technologies. However, owing to the complexity and individuation of a patient's clinical phenotypes, current prescription recommendation methods cannot obtain good performance. Meanwhile, it is very difficult to conduct effective representation for unrecorded symptom terms in an existing knowledge base. In this study, we proposed a subnetwork-based symptom term mapping method (SSTM) and constructed a SSTM-based TCM prescription recommendation method (termed TCMPR). Our SSTM can extract the subnetwork structure between symptoms from a knowledge network to effectively represent the embedding features of clinical symptom terms (especially the unrecorded terms). The experimental results showed that our method performs better than state-of-the-art methods. In addition, the comprehensive experiments of TCMPR with different hyperparameters (i.e., feature embedding, feature dimension, subnetwork filter threshold, and feature fusion) demonstrate that our method has high performance on TCM prescription recommendation and potentially promote clinical diagnosis and treatment of TCM precision medicine.
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