In the process of solving the control tasks of complex objects, the persons making the decision often have to deal with the uncertainty of the environment fimctioning, for example, in economics and management, they ha...In the process of solving the control tasks of complex objects, the persons making the decision often have to deal with the uncertainty of the environment fimctioning, for example, in economics and management, they have to make decisions in an uncertain state of the financial assets, economic environment, and so on. The modem design of decision-making under uncertainty is closely related to the application of fuzzy set theory which was developed by American scientist Zadeh. Experts＇ evaluation of alternatives for a variety of measure for decision-making can be represented as fuzzy sets or numbers expressed using membership functions. The theory of fuzzy sets has found its application in different fields of mathematics, biology, psychology, linguistics, and other application areas. In this paper, authors are interested in determining of fuzzy modeling in management and other relevant science （economic, financial, and ecological） and would have vector options to efficiency and environment program.展开更多
The intellectual property protection, whether judicial or administrative, is evaluated through a performance evaluation indicator system. To building up such a system, we must follow certain working procedures which u...The intellectual property protection, whether judicial or administrative, is evaluated through a performance evaluation indicator system. To building up such a system, we must follow certain working procedures which usually consist of four steps： to determine the performance objectives, to design the structure of indicator system, to specify the indicators and to set up the weight of indicators. Each step plays a different role in performance evaluation indicator system and has its own impact on the realization of performance evaluation objectives respectively. So the scientifically building up a performance evaluation indicator system is the key to determine whether the intellectual property is protected well or not.展开更多
Background Biomarkers for predicting suicide risk in hospitalised patients with mental disorders have been understudied.Currently,suicide risk assessment tools based on objective indicators are limited in China.Aims T...Background Biomarkers for predicting suicide risk in hospitalised patients with mental disorders have been understudied.Currently,suicide risk assessment tools based on objective indicators are limited in China.Aims To examine the value of various biomarkers in suicide risk prediction and develop a risk assessment model with clinical utility using machine learning.Methods This cohort study analysed patients with major depressive disorder(MDD)who were hospitalised for the first time between January 2016 and March 2023 from four specialised mental health institutions.A total of 139 features,including biomarker measurements,medical orders and psychological scales,were assessed for analysis.Their suicide risk was evaluated by qualified nurses using Nurse's Global Assessment of Suicide Risk within 1 week after admission.Five machine learning models were trained with 10-fold cross-validation across three hospitals and were externally validated in an independent cohort.The primary performance was assessed using the area under the receiver operating characteristic curve(AUR0C).The model was interpreted using the SHapley Additive exPlanations(SHAP)analysis.Biomarker importance was evaluated by comparing model performance with and without these biomarkers.Results Of 3143 patients with MDD included in this study,the incidence of high suicide risk within 1 week after first admission was 660(21.0%).Among all models,the Extreme Gradient Boosting can more effectively predict future risks,with an AUROC higher than 0.8(p<0.001).The SHAP values identified the 10 most important features,including five biomarkers.After clustering analysis,electroconvulsive therapy,physical restraint,β2-microglobulin and triiodothyronine were found to have heterogeneous effects on suicide risk.Combining biomarkers with other data from electronic health records significantly improved the performance and clinical utility of machine learning models based on demographics,diagnosis,laboratory tests,medical orders and psychological scales.Conclusions This study demonstrates the potential for a biomarker-based suicide risk assessment for patients with MDD,emphasising the interaction between biomarkers and therapeutic interventions.展开更多
文摘In the process of solving the control tasks of complex objects, the persons making the decision often have to deal with the uncertainty of the environment fimctioning, for example, in economics and management, they have to make decisions in an uncertain state of the financial assets, economic environment, and so on. The modem design of decision-making under uncertainty is closely related to the application of fuzzy set theory which was developed by American scientist Zadeh. Experts＇ evaluation of alternatives for a variety of measure for decision-making can be represented as fuzzy sets or numbers expressed using membership functions. The theory of fuzzy sets has found its application in different fields of mathematics, biology, psychology, linguistics, and other application areas. In this paper, authors are interested in determining of fuzzy modeling in management and other relevant science （economic, financial, and ecological） and would have vector options to efficiency and environment program.
文摘The intellectual property protection, whether judicial or administrative, is evaluated through a performance evaluation indicator system. To building up such a system, we must follow certain working procedures which usually consist of four steps： to determine the performance objectives, to design the structure of indicator system, to specify the indicators and to set up the weight of indicators. Each step plays a different role in performance evaluation indicator system and has its own impact on the realization of performance evaluation objectives respectively. So the scientifically building up a performance evaluation indicator system is the key to determine whether the intellectual property is protected well or not.
基金supported by projects from Shanghai Putuo District Municipal Health Committee(ptkwws202413)Shanghai Municipal Health Commission(202340018)+2 种基金Shanghai Hospital Development Center(Data Sharing and Emulation of Clinical Trials,CCS-DASET:SHDC2024CRI008)Shanghai Changning District Municipal Commission of Health(CNWJXY026)School of Innovation and Entrepreneurship,Tongji University(S202310247388,X2024085 and X2024048).
文摘Background Biomarkers for predicting suicide risk in hospitalised patients with mental disorders have been understudied.Currently,suicide risk assessment tools based on objective indicators are limited in China.Aims To examine the value of various biomarkers in suicide risk prediction and develop a risk assessment model with clinical utility using machine learning.Methods This cohort study analysed patients with major depressive disorder(MDD)who were hospitalised for the first time between January 2016 and March 2023 from four specialised mental health institutions.A total of 139 features,including biomarker measurements,medical orders and psychological scales,were assessed for analysis.Their suicide risk was evaluated by qualified nurses using Nurse's Global Assessment of Suicide Risk within 1 week after admission.Five machine learning models were trained with 10-fold cross-validation across three hospitals and were externally validated in an independent cohort.The primary performance was assessed using the area under the receiver operating characteristic curve(AUR0C).The model was interpreted using the SHapley Additive exPlanations(SHAP)analysis.Biomarker importance was evaluated by comparing model performance with and without these biomarkers.Results Of 3143 patients with MDD included in this study,the incidence of high suicide risk within 1 week after first admission was 660(21.0%).Among all models,the Extreme Gradient Boosting can more effectively predict future risks,with an AUROC higher than 0.8(p<0.001).The SHAP values identified the 10 most important features,including five biomarkers.After clustering analysis,electroconvulsive therapy,physical restraint,β2-microglobulin and triiodothyronine were found to have heterogeneous effects on suicide risk.Combining biomarkers with other data from electronic health records significantly improved the performance and clinical utility of machine learning models based on demographics,diagnosis,laboratory tests,medical orders and psychological scales.Conclusions This study demonstrates the potential for a biomarker-based suicide risk assessment for patients with MDD,emphasising the interaction between biomarkers and therapeutic interventions.
