Background and aim Recently,long-term outcomes in patients with spontaneous intracerebral haemorrhage(sICH)have gained increasing attention besides acute-phase characteristics.Predictive models for long-term outcomes ...Background and aim Recently,long-term outcomes in patients with spontaneous intracerebral haemorrhage(sICH)have gained increasing attention besides acute-phase characteristics.Predictive models for long-term outcomes are valuable for risk stratification and treatment strategies.This study aimed to develop and validate an explainable model for predicting long-term recurrence and all-cause death in patients with ICH,using clinical and imaging markers of cerebral small vascular diseases from MRI.Method We retrospectively analysed data from a prospectively collected large-scale cohort of patients with acute ICH admitted to the Neurology Department of The Second Affiliated Hospital of Zhejiang University between November 2016 and April 2023.After comprehensive variable selection using least absolute shrinkage and selection operator and stepwise Cox regression,we constructed Cox proportional hazards models to predict recurrence and all-cause death.Model performance was evaluated using the concordance index,integrated Brier score and time-dependent area under the curve.Global and local interpretability were assessed using variable importance calculated as SurvSHAP(t)and SurvLIME methods for the entire training set and individual patients,respectively.Results A total of 842 eligible patients were included.Over a median follow-up of 36 months(IQR:12-51),86 patients(9.1%)died,and 62 patients(6.6%)experienced recurrence of ICH.The concordance indexes for the all-cause death and recurrence models were 0.841(95%CI 0.767 to 0.913)and 0.759(95%CI 0.651 to 0.867),respectively,with integrated Brier scores of 0.079 and 0.063.The interpretability maps highlighted age,aetiology of ICH and low haemoglobin as key predictors of long-term death,while cortical superficial siderosis and previous haemorrhage were crucial for predicting recurrence.Conclusions This model demonstrates high predictive accuracy and emphasises the crucial factors in predicting long-term outcomes of patients with sICH.展开更多
The high porosity and tunable chemical functionality of metal-organic frameworks(MOFs)make it a promising catalyst design platform.High-throughput screening of catalytic performance is feasible since the large MOF str...The high porosity and tunable chemical functionality of metal-organic frameworks(MOFs)make it a promising catalyst design platform.High-throughput screening of catalytic performance is feasible since the large MOF structure database is available.In this study,we report a machine learning model for high-throughput screening of MOF catalysts for the CO_(2) cycloaddition reaction.The descriptors for model training were judiciously chosen according to the reaction mechanism,which leads to high accuracy up to 97%for the 75%quantile of the training set as the classification criterion.The feature contribution was further evaluated with SHAP and PDP analysis to provide a certain physical understanding.12,415 hypothetical MOF structures and 100 reported MOFs were evaluated under 100℃ and 1 bar within one day using the model,and 239 potentially efficient catalysts were discovered.Among them,MOF-76(Y)achieved the top performance experimentally among reported MOFs,in good agreement with the prediction.展开更多
Stainless-steel provides substantial advantages for structural uses,though its upfront cost is notably high.Consequently,it’s vital to establish safe and economically viable design practices that enhance material uti...Stainless-steel provides substantial advantages for structural uses,though its upfront cost is notably high.Consequently,it’s vital to establish safe and economically viable design practices that enhance material utilization.Such development relies on a thorough understanding of the mechanical properties of structural components,particularly connections.This research advances the field by investigating the behavior of stainless-steel connections through the use of a four-parameter fitting technique and explainable artificial intelligence methods.Training was conducted on eight different machine learning algorithms,namely,Decision Tree,Random Forest,K-nearest neighbors,Gradient Boosting,Extreme Gradient Boosting,Light Gradient Boosting,Adaptive Boosting,and Categorical Boosting.SHapley Additive Explanations was applied to interpret model predictions,highlighting features like spacing between bolts in tension and end-plate height as highly impactful on the initial rotational stiffness and plastic moment resistance.Results showed that Extreme Gradient Boosting achieved a coefficient of determination score of 0.99 for initial stiffness and plastic moment resistance,while Gradient Boosting model had similar performance with maximum moment resistance and ultimate rotation.A user-friendly graphical user interface(GUI)was also developed,allowing engineers to input parameters and get rapid moment–rotation predictions.This framework offers a data-driven,interpretable alternative to conventional methods,supporting future design recommendations for stainless-steel beam-to-column connections.展开更多
Advanced machine learning(ML)algorithms have outperformed traditional approaches in various forecasting applications,especially electricity price forecasting(EPF).However,the prediction accuracy of ML reduces substant...Advanced machine learning(ML)algorithms have outperformed traditional approaches in various forecasting applications,especially electricity price forecasting(EPF).However,the prediction accuracy of ML reduces substantially if the input data is not similar to the ones seen by the model during training.This is often observed in EPF problems when market dynamics change owing to a rise in fuel prices,an increase in renewable penetration,a change in operational policies,etc.While the dip in model accuracy for unseen data is a cause for concern,what is more,challenging is not knowing when the ML model would respond in such a manner.Such uncertainty makes the power market participants,like bidding agents and retailers,vulnerable to substantial financial loss caused by the prediction errors of EPF models.Therefore,it becomes essential to identify whether or not the model prediction at a given instance is trustworthy.In this light,this paper proposes a trust algorithm for EPF users based on explainable artificial intelligence techniques.The suggested algorithm generates trust scores that reflect the model’s prediction quality for each new input.These scores are formulated in two stages:in the first stage,the coarse version of the score is formed using correlations of local and global explanations,and in the second stage,the score is fine-tuned further by the Shapley additive explanations values of different features.Such score-based explanations are more straightforward than feature-based visual explanations for EPF users like asset managers and traders.A dataset from Italy’s and ERCOT’s electricity market validates the efficacy of the proposed algorithm.Results show that the algorithm has more than 85%accuracy in identifying good predictions when the data distribution is similar to the training dataset.In the case of distribution shift,the algorithm shows the same accuracy level in identifying bad predictions.展开更多
The COVID-19 pandemic,caused by the severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),triggered a global emergency that exposed the urgent need for surveillance approaches to monitor the dynamics of viral tr...The COVID-19 pandemic,caused by the severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),triggered a global emergency that exposed the urgent need for surveillance approaches to monitor the dynamics of viral transmission.Several epidemiological tools that may help anticipate outbreaks have been developed.Wastewater-based epidemiology is a non-invasive and population-wide methodology for tracking the epidemiological evolution of the virus.However,thorough evaluation and understanding of the limitations,robustness,and intricacies of wastewater-based epidemiology are still pending to effectively use this strategy.The aim of this study was to train highly accurate predictive models using SARS-CoV-2 virus concentrations in wastewater in a region consisting of several municipalities.The chosen region was Catalonia(Spain)given the availability of wastewater SARSCoV-2 quantification from the Catalan surveillance network and healthcare data(clinical cases)from the regional government.By using various feature engineering and machine learning methods,we developed a model that can accurately predict and successfully generalize across the municipalities that make up Catalonia.Explainable Machine Learning frameworks were also used,which allowed us to understand the factors that influence decision-making.Our findings support wastewater-based epidemiology as a potential surveillance tool to assist public health authorities in anticipating and monitoring outbreaks.展开更多
基金supported by the Zhejiang Provincial Medical and Health Science and Technology Project(2022KY174)the‘Pioneer’R&D Program of Zhejiang(2024C03006).
文摘Background and aim Recently,long-term outcomes in patients with spontaneous intracerebral haemorrhage(sICH)have gained increasing attention besides acute-phase characteristics.Predictive models for long-term outcomes are valuable for risk stratification and treatment strategies.This study aimed to develop and validate an explainable model for predicting long-term recurrence and all-cause death in patients with ICH,using clinical and imaging markers of cerebral small vascular diseases from MRI.Method We retrospectively analysed data from a prospectively collected large-scale cohort of patients with acute ICH admitted to the Neurology Department of The Second Affiliated Hospital of Zhejiang University between November 2016 and April 2023.After comprehensive variable selection using least absolute shrinkage and selection operator and stepwise Cox regression,we constructed Cox proportional hazards models to predict recurrence and all-cause death.Model performance was evaluated using the concordance index,integrated Brier score and time-dependent area under the curve.Global and local interpretability were assessed using variable importance calculated as SurvSHAP(t)and SurvLIME methods for the entire training set and individual patients,respectively.Results A total of 842 eligible patients were included.Over a median follow-up of 36 months(IQR:12-51),86 patients(9.1%)died,and 62 patients(6.6%)experienced recurrence of ICH.The concordance indexes for the all-cause death and recurrence models were 0.841(95%CI 0.767 to 0.913)and 0.759(95%CI 0.651 to 0.867),respectively,with integrated Brier scores of 0.079 and 0.063.The interpretability maps highlighted age,aetiology of ICH and low haemoglobin as key predictors of long-term death,while cortical superficial siderosis and previous haemorrhage were crucial for predicting recurrence.Conclusions This model demonstrates high predictive accuracy and emphasises the crucial factors in predicting long-term outcomes of patients with sICH.
基金financial support from the National Key Research and Development Program of China(2021YFB 3501501)the National Natural Science Foundation of China(No.22225803,22038001,22108007 and 22278011)+1 种基金Beijing Natural Science Foundation(No.Z230023)Beijing Science and Technology Commission(No.Z211100004321001).
文摘The high porosity and tunable chemical functionality of metal-organic frameworks(MOFs)make it a promising catalyst design platform.High-throughput screening of catalytic performance is feasible since the large MOF structure database is available.In this study,we report a machine learning model for high-throughput screening of MOF catalysts for the CO_(2) cycloaddition reaction.The descriptors for model training were judiciously chosen according to the reaction mechanism,which leads to high accuracy up to 97%for the 75%quantile of the training set as the classification criterion.The feature contribution was further evaluated with SHAP and PDP analysis to provide a certain physical understanding.12,415 hypothetical MOF structures and 100 reported MOFs were evaluated under 100℃ and 1 bar within one day using the model,and 239 potentially efficient catalysts were discovered.Among them,MOF-76(Y)achieved the top performance experimentally among reported MOFs,in good agreement with the prediction.
文摘Stainless-steel provides substantial advantages for structural uses,though its upfront cost is notably high.Consequently,it’s vital to establish safe and economically viable design practices that enhance material utilization.Such development relies on a thorough understanding of the mechanical properties of structural components,particularly connections.This research advances the field by investigating the behavior of stainless-steel connections through the use of a four-parameter fitting technique and explainable artificial intelligence methods.Training was conducted on eight different machine learning algorithms,namely,Decision Tree,Random Forest,K-nearest neighbors,Gradient Boosting,Extreme Gradient Boosting,Light Gradient Boosting,Adaptive Boosting,and Categorical Boosting.SHapley Additive Explanations was applied to interpret model predictions,highlighting features like spacing between bolts in tension and end-plate height as highly impactful on the initial rotational stiffness and plastic moment resistance.Results showed that Extreme Gradient Boosting achieved a coefficient of determination score of 0.99 for initial stiffness and plastic moment resistance,while Gradient Boosting model had similar performance with maximum moment resistance and ultimate rotation.A user-friendly graphical user interface(GUI)was also developed,allowing engineers to input parameters and get rapid moment–rotation predictions.This framework offers a data-driven,interpretable alternative to conventional methods,supporting future design recommendations for stainless-steel beam-to-column connections.
文摘Advanced machine learning(ML)algorithms have outperformed traditional approaches in various forecasting applications,especially electricity price forecasting(EPF).However,the prediction accuracy of ML reduces substantially if the input data is not similar to the ones seen by the model during training.This is often observed in EPF problems when market dynamics change owing to a rise in fuel prices,an increase in renewable penetration,a change in operational policies,etc.While the dip in model accuracy for unseen data is a cause for concern,what is more,challenging is not knowing when the ML model would respond in such a manner.Such uncertainty makes the power market participants,like bidding agents and retailers,vulnerable to substantial financial loss caused by the prediction errors of EPF models.Therefore,it becomes essential to identify whether or not the model prediction at a given instance is trustworthy.In this light,this paper proposes a trust algorithm for EPF users based on explainable artificial intelligence techniques.The suggested algorithm generates trust scores that reflect the model’s prediction quality for each new input.These scores are formulated in two stages:in the first stage,the coarse version of the score is formed using correlations of local and global explanations,and in the second stage,the score is fine-tuned further by the Shapley additive explanations values of different features.Such score-based explanations are more straightforward than feature-based visual explanations for EPF users like asset managers and traders.A dataset from Italy’s and ERCOT’s electricity market validates the efficacy of the proposed algorithm.Results show that the algorithm has more than 85%accuracy in identifying good predictions when the data distribution is similar to the training dataset.In the case of distribution shift,the algorithm shows the same accuracy level in identifying bad predictions.
基金funded by the Valencian International University and Generalitat Valenciana(GVA)through the Grants to emerging research groups 2023(CE2023)from the Regional Ministry of Innovation,Universities,Science and Digital Society(CIGE/2022/58).
文摘The COVID-19 pandemic,caused by the severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),triggered a global emergency that exposed the urgent need for surveillance approaches to monitor the dynamics of viral transmission.Several epidemiological tools that may help anticipate outbreaks have been developed.Wastewater-based epidemiology is a non-invasive and population-wide methodology for tracking the epidemiological evolution of the virus.However,thorough evaluation and understanding of the limitations,robustness,and intricacies of wastewater-based epidemiology are still pending to effectively use this strategy.The aim of this study was to train highly accurate predictive models using SARS-CoV-2 virus concentrations in wastewater in a region consisting of several municipalities.The chosen region was Catalonia(Spain)given the availability of wastewater SARSCoV-2 quantification from the Catalan surveillance network and healthcare data(clinical cases)from the regional government.By using various feature engineering and machine learning methods,we developed a model that can accurately predict and successfully generalize across the municipalities that make up Catalonia.Explainable Machine Learning frameworks were also used,which allowed us to understand the factors that influence decision-making.Our findings support wastewater-based epidemiology as a potential surveillance tool to assist public health authorities in anticipating and monitoring outbreaks.
