Observational studyFrontiers of medicine2018
Multistage analysis method for detection of effective herb prescription from clinical data.
Observational study in Frontiers of medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- 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
- Model informed precision medicine of Chinese herbal medicines formulas-A multi-scale mechanistic intelligent model.Journal of pharmaceutical analysis · 2024Article
- A Machine Learning Approach for Recommending Herbal Formulae with Enhanced Interpretability and Applicability.Biomolecules · 2022Article
- TCMPR: TCM Prescription Recommendation Based on Subnetwork Term Mapping and Deep Learning.BioMed research international · 2022Article
- End-to-End Models to Imitate Traditional Chinese Medicine Syndrome Differentiation in Lung Cancer Diagnosis: Model Development and Validation.JMIR medical informatics · 2020Article
- Heterogeneous network propagation for herb target identification.BMC medical informatics and decision making · 2018Article
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Authors and funding
12 authors.
Funding
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Abstract
Determining effective traditional Chinese medicine (TCM) treatments for specific disease conditions or particular patient groups is a difficult issue that necessitates investigation because of the complicated personalized manifestations in real-world patients and the individualized combination therapies prescribed in clinical settings. In this study, a multistage analysis method that integrates propensity case matching, complex network analysis, and herb set enrichment analysis was proposed to identify effective herb prescriptions for particular diseases (e.g., insomnia). First, propensity case matching was applied to match clinical cases. Then, core network extraction and herb set enrichment were combined to detect core effective herb prescriptions. Effectiveness-based mutual information was used to detect strong herb-symptom relationships. This method was applied on a TCM clinical data set with 955 patients collected from well-designed observational studies. Results revealed that groups of herb prescriptions with higher effectiveness rates (76.9% vs. 42.8% for matched samples; 94.2% vs. 84.9% for all samples) compared with the original prescriptions were found. Particular patient groups with symptom manifestations were also identified to help investigate the indications of the effective herb prescriptions.
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