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Chin Med Sci J ; 35(4): 297-305, 2020 Dec 31.
Artigo em Inglês | MEDLINE | ID: mdl-33413745


Objective Asymptomatic carotid stenosis (ACS) is closely associated to the incidence of severe cerebrovascular diseases. Early identifying the individuals with ACS and its associated risk factors could be beneficial for primary prevention of stroke. This study aimed to investigate a machine-learning algorithm for the detection of ACS among high-risk population of stroke based on the associated risk factors.Methods A novel model of machine learning was utilized to screen the associated predictors of ACS based on 30 potential risk factors. The algorithm of this model adopted a random forest pattern based on the training data and then was verified using the testing data. All of the original data were retrieved from the China National Stroke Screening and Prevention Project (CNSSPP), including demographic, clinical and laboratory characteristics. The individuals with high risk of stroke were enrolled and randomly divided into a training group and a testing group at a ratio of 4:1. The identification of carotid stenosis by carotid artery duplex scans was set as the golden standard. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) was used to evaluate the efficacy of the model in detecting ACS.Results Of 2841 high risk individual of stroke enrolled, 326 (11.6%) were diagnosed as ACS by ultrasonography. The top five risk factors contributing to ACS in this model were identified as family history of dyslipidemia, high level of low-density lipoprotein cholesterol (LDL-c), low level of high-density lipoprotein cholesterol (HDL-c), aging, and low body mass index (BMI). Their weights were 11.8%, 7.6%, 7.1%, 6.1%, and 6.1%, respectively. The total weight of the top 15 risk factors was 85.5%. The AUC values of the model for detecting ACS with training dataset and testing dataset were 0.927 and 0.888, respectively.Conclusions This study demonstrated that the machine-learning algorithm could be used to identify the risk factors for ACS among high risk population of stroke. Family history of dyslipidemia may be the most important risk factor for ACS. This model could be a suitable tool to optimize the clinical approach for the primary prevention of stroke.

Algoritmos , Estenose das Carótidas/diagnóstico , Estenose das Carótidas/etiologia , Aprendizado de Máquina , Acidente Vascular Cerebral/complicações , Árvores de Decisões , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Curva ROC , Fatores de Risco
Zhongguo Zhong Yao Za Zhi ; 42(9): 1787-1791, 2017 May.
Artigo em Chinês | MEDLINE | ID: mdl-29082708


In order to explore the compatible principles of Xiebai decoction family, formulae from ancient and modern Xiebai decoction family were collected and sorted in this study. The compatible characteristics, core herbs, as well as the relativity of herbs nature in Xiebai decoction family were analyzed based on scale free network and other data-mining methods such as association rules, clustering analysis and correspondence analysis. The scale free network results showed that in Xiebai decoction family, Mori Cortex-Lycii Cortex-Glycyrrhizae Radix et Rhizoma was used as the core compatible group and formed the complicated compatible network with other additional herbs; association rules results showed that the core herbs in such formulae included Mori Cortex, Lycii Cortex, Glycyrrhizae Radix et Rhizoma, scutellaria root, Platycodon root, Anemarrhena, and almond, which formed corresponding herbal pairs and compatibility; clustering analysis showed that Mori Cortex was the core herb in Xiebai decoction family, and Mori Cortex-Lycii Cortex-Glycyrrhizae Radix et Rhizoma was its main combination unit, which was always compatible with herbs of clearing heat, reducing phlegm, supplementing Qi and nourishing Yin to form the series prescriptions. The results indicated that the core compatibility features of Xiebai decoction family were clearing heat in lung and relieving cough and asthma, providing a basis for the clinical application of Xiebai decoction family.

Medicamentos de Ervas Chinesas/química , Extratos Vegetais/química , Mineração de Dados , Rizoma/química