With the continuous development of education in China,various vocational colleges have responded to the“Double-High Plan”and are actively building teaching innovation teams with“double-qualified teachers.”By impro...With the continuous development of education in China,various vocational colleges have responded to the“Double-High Plan”and are actively building teaching innovation teams with“double-qualified teachers.”By improving the professional level of teachers and increasing practical opportunities,vocational colleges and universities can nurture high-level teachers and improve their teaching level so as to meet the demand for high-level technical skills training in the new era.This paper discusses the significance of constructing a“double-qualified”education model under the background of the“Double-High Plan,”the requirements of the“double-qualified”education model for teachers,the problems in constructing a teaching innovation team with“double-qualified”teachers,and the countermeasures for these problems.展开更多
In recent years, cardiovascular and cerebrovascular diseases have seriously jeopardized people’s health. Double-high (hyperlipidemia and hypertension) is one of the main causes of cardiovascular and cerebrovascular d...In recent years, cardiovascular and cerebrovascular diseases have seriously jeopardized people’s health. Double-high (hyperlipidemia and hypertension) is one of the main causes of cardiovascular and cerebrovascular diseases in clinical practice. To diagnose cardiovascular and cerebrovascular diseases early, a reliable prediction system should be developed to assist doctors. In this paper, the different elements and evaluate the weight of these elements on double-high diseases are analyzed by machine learning method. The LightGBM algorithm and XGBoost algorithm were employed to construct the prediction models, respectively. Significantly, the proposed model was trained by real physical examination data and five meaningful and useful biochemical indicators were selected to encoding the raw physical examination data to numerical vector. The selected features are systolic blood pressure, diastolic blood pressure, serum triglyceride, serum high-density lipoprotein and serum low-density lipoprotein. The mean square error (MSE) after calculating the logarithm of the predicted value and the true value was introduced to assess the prediction model. Results show that this model can effectively predict cardiovascular and cerebrovascular diseases in advance.展开更多
文摘With the continuous development of education in China,various vocational colleges have responded to the“Double-High Plan”and are actively building teaching innovation teams with“double-qualified teachers.”By improving the professional level of teachers and increasing practical opportunities,vocational colleges and universities can nurture high-level teachers and improve their teaching level so as to meet the demand for high-level technical skills training in the new era.This paper discusses the significance of constructing a“double-qualified”education model under the background of the“Double-High Plan,”the requirements of the“double-qualified”education model for teachers,the problems in constructing a teaching innovation team with“double-qualified”teachers,and the countermeasures for these problems.
基金the National Natural Science Foundation of China (Nos. 61772227,61702214)the Development Project of Jilin Province of China (Nos 20170101006JC, 20170203002GX, 20190201293JC). This work was also supported by Jilin Provincial Key Laboratory of Big Date Intelligent Computing (No. 20180622002JC).
文摘In recent years, cardiovascular and cerebrovascular diseases have seriously jeopardized people’s health. Double-high (hyperlipidemia and hypertension) is one of the main causes of cardiovascular and cerebrovascular diseases in clinical practice. To diagnose cardiovascular and cerebrovascular diseases early, a reliable prediction system should be developed to assist doctors. In this paper, the different elements and evaluate the weight of these elements on double-high diseases are analyzed by machine learning method. The LightGBM algorithm and XGBoost algorithm were employed to construct the prediction models, respectively. Significantly, the proposed model was trained by real physical examination data and five meaningful and useful biochemical indicators were selected to encoding the raw physical examination data to numerical vector. The selected features are systolic blood pressure, diastolic blood pressure, serum triglyceride, serum high-density lipoprotein and serum low-density lipoprotein. The mean square error (MSE) after calculating the logarithm of the predicted value and the true value was introduced to assess the prediction model. Results show that this model can effectively predict cardiovascular and cerebrovascular diseases in advance.
