ArticleEvidence-based complementary and alternative medicine : eCAM2022
Predicting Coupled Herbs for the Treatment of Hypertension Complicated with Coronary Heart Disease in Real-World Data Based on a Complex Network and Machine Learning.
Article in Evidence-based complementary and alternative medicine : eCAM, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 11 citations in OpenAlex.
- Development and validation of a deep neural network for predicting coronary heart disease in hypertensive patients using 24-hour ambulatory blood pressure monitoring: a retrospective study.Frontiers in medicine · 2026Article
- Anti-hypertensive effect and potential mechanism of gastrodia-uncaria granules based on network pharmacology and experimental validation.Journal of clinical hypertension (Greenwich, Conn.) · 2024Article
- Shapely additive values can effectively visualize pertinent covariates in machine learning when predicting hypertension.Journal of clinical hypertension (Greenwich, Conn.) · 2023Article
- Effect of botanical drugs in improving symptoms of hypertensive nephropathy: Analysis of real-world data, retrospective cohort, network, and experimental assessment.Frontiers in pharmacology · 2023Article
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
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Abstract
Hypertension and coronary heart disease are the most common cardiovascular diseases, and traditional Chinese medicine is applied as an auxiliary treatment for common cardiovascular diseases. This study is based on 3 years of electronic medical record data from the Affiliated Hospital of Shandong University of Traditional Chinese Medicine. A complex network and machine learning algorithm were used to establish a screening model of coupled herbs for the treatment of hypertension complicated with coronary heart disease. A total of 5688 electronic medical records were collected to establish the prescription network and symptom database. The hierarchical network extraction algorithm was used to obtain core herbs. Biological features of herbs were collected from public databases. At the same time, five supervised machine learning models were established based on the biological features of the coupled herbs. Finally, the K-nearest neighbor model was established as a screening model with an AUROC of 91.0%. Seventy coupled herbs for adjuvant treatment of hypertension complicated with coronary heart disease were obtained. It was found that the coupled herbs achieved the purpose of adjuvant therapy mainly by interfering with cytokines and regulating inflammatory and metabolic pathways. These results show that this model can integrate the molecular biological characteristics of herbs, preliminarily screen combinations of herbs, and provide ideas for explaining the value in clinical applications.
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Registered trials
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