Background Identification of futile recanalisation following endovascular therapy(EVT)in patients with acute ischaemic stroke is both crucial and challenging.Here,we present a novel risk stratification system based on...Background Identification of futile recanalisation following endovascular therapy(EVT)in patients with acute ischaemic stroke is both crucial and challenging.Here,we present a novel risk stratification system based on hybrid machine learning method for predicting futile recanalisation.Methods Hybrid machine learning models were developed to address six clinical scenarios within the EVT and perioperative management workflow.These models were trained on a prospective database using hybrid feature selection technique to predict futile recanalisation following EVT.The optimal model was validated and compared with existing models and scoring systems in a multicentre prospective cohort to develop a hybrid machine learning-based risk stratification system for futile recanalisation prediction.Results Using a hybrid feature selection approach,we trained and tested multiple classifiers on two independent patient cohorts(n=1122)to develop a hybrid machine learning-based prediction model.The model demonstrated superior discriminative ability compared with other models and scoring systems(area under the curve=0.80,95%CI 0.73 to 0.87)and was transformed into a web application(RESCUE-FR Index)that provides a risk stratification system for individual prediction(accessible online atfr-index.biomind.cn/RESCUE-FR/).Conclusions The proposed hybrid machine learning approach could be used as an individualised risk prediction model to facilitate adherence to clinical practice guidelines and shared decision-making for optimal candidate selection and prognosis assessment in patients undergoing EVT.展开更多
The restructuring of the energy industry is imperative,as New Zealand strives to reduce greenhouse gas emissions.New Zealand has abundant renewable energy resources,and about 85%of current electricity generation is fr...The restructuring of the energy industry is imperative,as New Zealand strives to reduce greenhouse gas emissions.New Zealand has abundant renewable energy resources,and about 85%of current electricity generation is from renewable energy sources.However,in recent years,it appears that a considerable fraction of wind energy has been underutilized.This article reviews the history,current status,and future trends of wind energy development in New Zealand.The main challenges to the current development of wind energy are summarized compared to other countries.The main challenges come from the bi-cultural influence,environmental influence,and economic and social influence due to the variable nature of wind power,it is critical to store and operate power safely and reliably during peak power generation periods.This article compares seven mainstream wind energy storage technologies and analyzes the best solution for wind energy storage in New Zealand.This article analyzes the feasibility of using small-scale household(standard power rating range from 0.004 to 16 kW)wind turbines in New Zealand cities regarding their construction and operation process.The life cycle and the maximum capacity coefficient of such small-scale wind turbines are overviewed via three case studies and later compared with large commercial wind turbines(standard power rating ranges from 1 to 3 MW)in power generation capacity.It has been found that small-scale household wind turbines have notable power generation potential and economic benefits in the long term.展开更多
基金National Natural Science Foundation of China(82001920,82071301,81820108012)Beijing Municipal Administration of Hospitals’Youth Programme(QML20210503).
文摘Background Identification of futile recanalisation following endovascular therapy(EVT)in patients with acute ischaemic stroke is both crucial and challenging.Here,we present a novel risk stratification system based on hybrid machine learning method for predicting futile recanalisation.Methods Hybrid machine learning models were developed to address six clinical scenarios within the EVT and perioperative management workflow.These models were trained on a prospective database using hybrid feature selection technique to predict futile recanalisation following EVT.The optimal model was validated and compared with existing models and scoring systems in a multicentre prospective cohort to develop a hybrid machine learning-based risk stratification system for futile recanalisation prediction.Results Using a hybrid feature selection approach,we trained and tested multiple classifiers on two independent patient cohorts(n=1122)to develop a hybrid machine learning-based prediction model.The model demonstrated superior discriminative ability compared with other models and scoring systems(area under the curve=0.80,95%CI 0.73 to 0.87)and was transformed into a web application(RESCUE-FR Index)that provides a risk stratification system for individual prediction(accessible online atfr-index.biomind.cn/RESCUE-FR/).Conclusions The proposed hybrid machine learning approach could be used as an individualised risk prediction model to facilitate adherence to clinical practice guidelines and shared decision-making for optimal candidate selection and prognosis assessment in patients undergoing EVT.
基金We gratefully acknowledge the financial support from the University of Canterbury,New Zealand(grant no.452DISDZ)Singapore National Research Foundation(grant no.NRF2016-NRF-NSFC001-102)+2 种基金The first author would like to thank the financial support by National Natural Science Foundtion of China(51775188,21805244)Natural Science Foundation of Zhejiang Province(LQ21E060003,LZ21E060001)。
文摘The restructuring of the energy industry is imperative,as New Zealand strives to reduce greenhouse gas emissions.New Zealand has abundant renewable energy resources,and about 85%of current electricity generation is from renewable energy sources.However,in recent years,it appears that a considerable fraction of wind energy has been underutilized.This article reviews the history,current status,and future trends of wind energy development in New Zealand.The main challenges to the current development of wind energy are summarized compared to other countries.The main challenges come from the bi-cultural influence,environmental influence,and economic and social influence due to the variable nature of wind power,it is critical to store and operate power safely and reliably during peak power generation periods.This article compares seven mainstream wind energy storage technologies and analyzes the best solution for wind energy storage in New Zealand.This article analyzes the feasibility of using small-scale household(standard power rating range from 0.004 to 16 kW)wind turbines in New Zealand cities regarding their construction and operation process.The life cycle and the maximum capacity coefficient of such small-scale wind turbines are overviewed via three case studies and later compared with large commercial wind turbines(standard power rating ranges from 1 to 3 MW)in power generation capacity.It has been found that small-scale household wind turbines have notable power generation potential and economic benefits in the long term.
