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.
Highlights
Introduction10.4 million people die of complications of hypertension worldwide [1]. Organ damage caused by hypertension and cardiovascular disease (CVD) are currently the main causes of death [2]
Every year, 10.4 million people die of complications of hypertension worldwide [1]
5688 electronic medical records (EMRs) of hypertension complicated with coronary heart disease (CHD) collected from the Affiliated Hospital of Shandong University of Traditional Chinese Medicine were used to extract and standardize the symptoms in the prescription and medical history of Traditional Chinese medicine (TCM) [14], and the prescription database and symptom database were established
Summary
10.4 million people die of complications of hypertension worldwide [1]. Organ damage caused by hypertension and cardiovascular disease (CVD) are currently the main causes of death [2]. Traditional Chinese medicine (TCM) is recommended as a complementary and alternative therapy in the treatment of hypertension and CHD in China. Erefore, we developed a mining method based on RWD to explore effective coupled herbs for the treatment of hypertension complicated with CHD by combining their symptom information and target information. It is still unable to fully evaluate the closeness of herb combinations and complicated diseases, including the target similarity of different herbs and their contribution to the curative effect. We established a prescription database and symptom database of patients with hypertension complicated with CHD. We used the hierarchical network extraction algorithm to extract the main herbs and symptoms from the database, collected biological information, established a biological network, including herbal compounds, targets, and related disease symptom information, and used supervised machine learning models compared with the classical Apriori algorithm. We used the hierarchical network extraction algorithm to extract the main herbs and symptoms from the database, collected biological information, established a biological network, including herbal compounds, targets, and related disease symptom information, and used supervised machine learning models compared with the classical Apriori algorithm. e best model was used to evaluate the pertinence of each coupled herb in the treatment of hypertension complicated with CHD
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