Objective:To investigate the diagnostic value of the monocyte-to-large-platelet ratio(MLPR),neutrophil-to-lymphocyte ratio(NLR),and red blood cell distribution width(RDW)for pulmonary embolism(PE)in patients with acut...Objective:To investigate the diagnostic value of the monocyte-to-large-platelet ratio(MLPR),neutrophil-to-lymphocyte ratio(NLR),and red blood cell distribution width(RDW)for pulmonary embolism(PE)in patients with acute exacerbation of chronic obstructive pulmonary disease(AECOPD).Methods:A total of 60 elderly AECOPD patients were enrolled and divided into embolus group(12 cases)and thrombus group(48 cases)according to whether they were combined with pulmonary embolism and the MLPR,NLR,and RDW values of the two groups were determined respectively.Results:The patients in the two groups had different degrees of vascular structural and functional abnormalities,and the MLPR,NLR,and RDW in the embolus group were significantly higher than those in the thrombus group(P<0.05);while the differences in NLR and RDW between the two groups were not significant.Conclusion:MLPR,NLR,and RDW can provide an objective basis for assessing PE in elderly AECOPD patients.展开更多
Model accuracy and runtime are two key issues for flood warnings in rivers.Traditional hydrodynamic models,which have a rigorous physical mechanism for flood routine,have been widely adopted for water level prediction...Model accuracy and runtime are two key issues for flood warnings in rivers.Traditional hydrodynamic models,which have a rigorous physical mechanism for flood routine,have been widely adopted for water level prediction in river,lake,and urban areas.However,these models require various types of data,in-depth domain knowledge,experience with modeling,and intensive computational time,which hinders short-term or real-time prediction.In this paper,we propose a new framework based on machine learning methods to alleviate the aforementioned limitation.We develop a wide range of machine learning models such as linear regression(LR),support vector regression(SVR),random forest regression(RFR),multilayer perceptron regression(MLPR),and light gradient boosting machine regression(LGBMR)to predict the hourly water level at Le Thuy and Kien Giang stations of the Kien Giang river based on collected data of 2010,2012,and 2020.Four evaluation metrics,that is,R^(2),Nash-Sutcliffe efficiency,mean absolute error,and root mean square error,are employed to examine the reliability of the proposed models.The results show that the LR model outperforms the SVR,RFR,MLPR,and LGBMR models.展开更多
文摘Objective:To investigate the diagnostic value of the monocyte-to-large-platelet ratio(MLPR),neutrophil-to-lymphocyte ratio(NLR),and red blood cell distribution width(RDW)for pulmonary embolism(PE)in patients with acute exacerbation of chronic obstructive pulmonary disease(AECOPD).Methods:A total of 60 elderly AECOPD patients were enrolled and divided into embolus group(12 cases)and thrombus group(48 cases)according to whether they were combined with pulmonary embolism and the MLPR,NLR,and RDW values of the two groups were determined respectively.Results:The patients in the two groups had different degrees of vascular structural and functional abnormalities,and the MLPR,NLR,and RDW in the embolus group were significantly higher than those in the thrombus group(P<0.05);while the differences in NLR and RDW between the two groups were not significant.Conclusion:MLPR,NLR,and RDW can provide an objective basis for assessing PE in elderly AECOPD patients.
基金Scientific Research and Technology Development Project。
文摘Model accuracy and runtime are two key issues for flood warnings in rivers.Traditional hydrodynamic models,which have a rigorous physical mechanism for flood routine,have been widely adopted for water level prediction in river,lake,and urban areas.However,these models require various types of data,in-depth domain knowledge,experience with modeling,and intensive computational time,which hinders short-term or real-time prediction.In this paper,we propose a new framework based on machine learning methods to alleviate the aforementioned limitation.We develop a wide range of machine learning models such as linear regression(LR),support vector regression(SVR),random forest regression(RFR),multilayer perceptron regression(MLPR),and light gradient boosting machine regression(LGBMR)to predict the hourly water level at Le Thuy and Kien Giang stations of the Kien Giang river based on collected data of 2010,2012,and 2020.Four evaluation metrics,that is,R^(2),Nash-Sutcliffe efficiency,mean absolute error,and root mean square error,are employed to examine the reliability of the proposed models.The results show that the LR model outperforms the SVR,RFR,MLPR,and LGBMR models.
