检索结果-南通市图书馆

Interpretable and Explainable Machine Learning for Materials Science and Chemistry

Accounts of Materials Research 2022年第6期3卷 597-607页

作者： Felipe Oviedo juan lavista ferres Tonio Buonassisi Keith T.Butler Massachusetts Institute of Technology CambridgeMassachusetts 02139United States Microsoft AI for Good Research Lab RedmondWashington 98052United States SciML Scientific Computing DepartmentRutherford Appleton LaboratoryDidcot OX110DU.K. Department of Chemistry University of ReadingReading RG66ADU.K.

Machine learning has become a common and powerful tool in materials *** more data become available,with the use of high-performance computing and high-throughput experimentation,machine learning has proven potential to accelerate scientific research and technology *** the uptake of data-driven approaches for materials science is at an exciting,early stage,to realize the true potential of machine learning models for successful scientific discovery,they must have qualities beyond purely predictive *** predictions and inner workings of models should provide a certain degree of explainability by human experts,permitting the identification of potential model issues or limitations,building trust in model predictions,and unveiling unexpected correlations that may lead to scientific *** this work,we summarize applications of interpretability and explainability techniques for materials science and chemistry and discuss how these techniques can improve the outcome of scientific *** start by defining the fundamental concepts of interpretability and explainability in machine learning and making them less abstract by providing examples in the *** show how interpretability in scientific machine learning has additional constraints compared to general *** upon formal definitions in machine learning,we formulate the basic trade-offs among the explainability,completeness,and scientific validity of model explanations in scientific *** the context of these trade-offs,we discuss how interpretable models can be constructed,what insights they provide,and what drawbacks they *** present numerous examples of the application of interpretable machine learning in a variety of experimental and simulation studies,encompassing first-principles calculations,physicochemical characterization,materials development,and integration into complex *** discuss the varied impacts and uses of interpretabiltiy in these cases

关键词： explain purely insight

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

A novel mortality risk score for emphysematous pyelonephritis:A multicenter study of the Global Research in the Emphysematous Pyelonephritis group

引用

Current Urology 2024年第1期18卷 55-60页

作者： Hegel Trujillo-Santamaría José Iván Robles-Torres Jeremy Yuen-Chun Teoh Yiloren Tanidir José Gadú Campos-Salcedo Edgar Iván Bravo-Castro Marcelo Langer Wroclawski W.S.Yeoh Santosh Kumar juan Eduardo Sanchez-Nuñez José Enrique Espinoza-Aznar Deepak Ragoori Saeed Bin Hamri Ong Teng Aik Cecil Paul Tarot-Chocooj Anil Shrestha Mohamed Amine Lakmichi Mateus Cosentino-Bellote Luis Gabriel Vázquez-lavista Boukary Kabre Ho Yee Tiong José Gustavo Arrambide-Herrera Lauro Salvador Gómez-Guerra Umut Kutukoglu Joao Arthur Brunhara Alves-Barbosa Jorge Jaspersen Christian Acevedo Francisco Virgen-Gutiérrez Sumit Agrawal Hugo Octaviano Duarte-Santos Chai Chu Ann Daniele Castellani Vineet Gahuar Department of Urology Hospital CovadongaCórdobaMéxico Department of Urology Hospital Universitario"Dr.José Eleuterio Gonzalez"MonterreyMéxico Department of Surgery S.H.Ho Urology CentrePrince of Wales HospitalThe Chinese University of Hong KongHong KongChina Department of Urology Marmara University School of MedicineIstanbulTurkey Department of Urology Hospital Central MilitarMéxico CityMéxico Department of Urology Hospital Israelita Albert EinsteinBP-a Beneficência Portuguesa de Sao PauloSao PauloBrazil Faculdade de Medicina do ABC Santo AndréBrazil Department of Urology University Surgical ClusterNational University HospitalSingaporeSingapore Department Urology Christian Medical CollegeVelloreTamilnadIndia Department Urology Hospital General de México"Dr.Eduardo Liceaga"México CityMéxico Department of Urology Hospital Regional de Alta Especialidad de la Peninsula de YucatánMéridaMéxico Department Urology Asian Institute of Nephrology and UrologyHyderabadIndia Department Urology King Abdulaziz National Guard HospitalRiyadhSaudi Arabia Division of Urology Department of SurgeryUniversity of MalayaKuala LumpurMalaysia Department of Urology Central Military HospitalMexico CityMexico National Academy of Medical Sciences Bir Hospital and B&B HospitalGwarko LalitpurNepal Department of Urology University Hospital Mohammed theⅥ^(th)of MarrakeshMarrakeshMorocco Department of Urology Federal University of ParanáSchool of MedicineHospital das ClínicasCuritibaBrazil Department of Urology Hospital Médica SurMexico CityMexico Department of Urology Hospital Yalgado Ouedraogo OuagadougaKadiogoBurkina Faso Department of Urology Hospital Das Clínicas da Universidade de Sao PauloSao PauloBrazil Department of Urology AIIMSBhubaneshwarIndia Department of Urology Hospital Municipal Dr.Moysés Deutsch(M'Boi Mirim)Sao PauloBrazil Department of Urology Azienda Ospedaliero-Universitaria Ospedali Riuniti di AnconaPolytechnic University of Le Marche RegionAnconaItaly Department of Minim

Background:Emphysematous pyelonephritis(EPN)is a necrotizing infection of the kidney and the surrounding tissues associated with considerable *** aimed to formulate a score that classifies the risk of mortality in patients with EPN at hospital *** and methods:Patients diagnosed with EPN between 2013 and 2020 were retrospectively *** from 15 centers(70%)were used to develop the scoring system,and data from 7 centers(30%)were used to validate *** and mułtivariable logistic regression analyses were performed to identify independent factors related to *** operating characteristic curve analysis was performed to construct the sconng system and calculate the risk of mortality.A standardized regression coefficient was used to quantify the discriminating power of each factor to convert the individual coefficients into *** area under the curve was used to quantify the scoring system *** 8-point scoring system for the mortality risk was created(range,0-7).Results:In total,570 patients were included(400 in the test group and 170 in the validation group).Independent predictors of mortality in the multivariable logistic regression were included in the scoring system:quick Sepsis-related Organ Failure Assessment score≥2(2 points),anemia,paranephric gas extension,leukocyte count>22,000/pL,thrombocytopenia,and hyperglycemia(1 point each).The mortality rate was<5%for scores≤3,83.3%for scores 6,and 100%for scores *** area under the Curve was 0.90(95%confidence interval,0.84-0.95)for test and 0.91(95%confidence interval,0.84-0.97)for the validation ***:Our score predicts the risk of mortality in patients with EPN at presentation and may help clinicians identify patients at a higher risk of death.

关键词： Emphysematous pyelonephritis Mortality Prognosis

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：