Currently,the decision of aortic valve replacement surgery time for asymptomatic patients with moderate-to-severe aortic stenosis(AS)is made by healthcare professionals based on the patient’s clinical biometric recor...Currently,the decision of aortic valve replacement surgery time for asymptomatic patients with moderate-to-severe aortic stenosis(AS)is made by healthcare professionals based on the patient’s clinical biometric records.A delay in surgical aortic valve replacement(SAVR)can potentially affect patients’quality of life.By using ML algorithms,this study aims to predict the optimal SAVR timing and determine the enhancement in moderate-to-severe AS patient survival following surgery.This study represents a novel approach that has the potential to improve decision-making and,ultimately,improve patient outcomes.We analyze data from 176 patients with moderate-to-severe aortic stenosis who had undergone or were indicated for SAVR.We divide the data into two groups:those who died within the first year after SAVR and those who survived for more than one year or were still alive at the last follow-up.We then use six different ML algorithms,Support Vector Machine(SVM),Classification and Regression Tree(C and R tree),Generalized Linear(GL),Chi-Square Automatic Interaction Detector(CHAID),Artificial Neural Net-work(ANN),and Linear Regression(LR),to generate predictions for the best timing for SAVR.The results showed that the SVM algorithm is the best model for predicting the optimal timing for SAVR and for predicting the post-surgery survival period.By optimizing the timing of SAVR surgery using the SVM algorithm,we observed a significant improvement in the survival period after SAVR.Our study demonstrates that ML algorithms generate reliable models for predicting the optimal timing of SAVR in asymptomatic patients with moderate-to-severe AS.展开更多
With the widespread application of the computer and microelectronic technology in the industry,digitization becomes the inevitable developing trend of the hydraulic technology.Digitization of the hydraulic components ...With the widespread application of the computer and microelectronic technology in the industry,digitization becomes the inevitable developing trend of the hydraulic technology.Digitization of the hydraulic components is critical in the digital hydraulic technology.High-speed on-of valves(HSVs)which convert a train of input pulses into the fast and accurate switching between the on and of states belong to widely used basic digital hydraulic elements.In some ways,the characteristics of the HSVs determine the performance of the digital hydraulic systems.This paper discusses the development of HSVs and their applications.First,the HSVs with innovative structures which is classifed into direct drive valves and pilot operated valves are discussed,with the emphasis on their performance.Then,an overview of HSVs with intelligent materials is presented with considering of the switching frequency and fow capacity.Finally,the applications of the HSVs are reviewed,including digital hydraulic components with the integration of the HSVs and digital hydraulic systems controlled by the HSVs.展开更多
文摘Currently,the decision of aortic valve replacement surgery time for asymptomatic patients with moderate-to-severe aortic stenosis(AS)is made by healthcare professionals based on the patient’s clinical biometric records.A delay in surgical aortic valve replacement(SAVR)can potentially affect patients’quality of life.By using ML algorithms,this study aims to predict the optimal SAVR timing and determine the enhancement in moderate-to-severe AS patient survival following surgery.This study represents a novel approach that has the potential to improve decision-making and,ultimately,improve patient outcomes.We analyze data from 176 patients with moderate-to-severe aortic stenosis who had undergone or were indicated for SAVR.We divide the data into two groups:those who died within the first year after SAVR and those who survived for more than one year or were still alive at the last follow-up.We then use six different ML algorithms,Support Vector Machine(SVM),Classification and Regression Tree(C and R tree),Generalized Linear(GL),Chi-Square Automatic Interaction Detector(CHAID),Artificial Neural Net-work(ANN),and Linear Regression(LR),to generate predictions for the best timing for SAVR.The results showed that the SVM algorithm is the best model for predicting the optimal timing for SAVR and for predicting the post-surgery survival period.By optimizing the timing of SAVR surgery using the SVM algorithm,we observed a significant improvement in the survival period after SAVR.Our study demonstrates that ML algorithms generate reliable models for predicting the optimal timing of SAVR in asymptomatic patients with moderate-to-severe AS.
基金Supported by Key Technologies Research and Development Program of China(Grant No.2019YFB2004502)National Natural Science Foundation of China(Grant Nos.51805350,51775362)Postdoctoral Science Foundation of China(Grant No.2019M651073).
文摘With the widespread application of the computer and microelectronic technology in the industry,digitization becomes the inevitable developing trend of the hydraulic technology.Digitization of the hydraulic components is critical in the digital hydraulic technology.High-speed on-of valves(HSVs)which convert a train of input pulses into the fast and accurate switching between the on and of states belong to widely used basic digital hydraulic elements.In some ways,the characteristics of the HSVs determine the performance of the digital hydraulic systems.This paper discusses the development of HSVs and their applications.First,the HSVs with innovative structures which is classifed into direct drive valves and pilot operated valves are discussed,with the emphasis on their performance.Then,an overview of HSVs with intelligent materials is presented with considering of the switching frequency and fow capacity.Finally,the applications of the HSVs are reviewed,including digital hydraulic components with the integration of the HSVs and digital hydraulic systems controlled by the HSVs.
