检索结果-内蒙古大学图书馆

Expected uncertainty reduction for sequential kriging-based reliability analysis

Expected uncertainty reduction for sequential kriging-based ...

ASME 2020 International Design Engineering Technical Conferences and computers and Information in Engineering Conference, IDETC-CIE 2020

作者： Li, Meng Shen, Sheng Barzegar, Vahid Sadoughi, Mohammadkazem Laflamme, Simon Hu, Chao Department of Mechanical Engineering Iowa State University AmesIA United States Corporate Engineering Data Science National Oilwell Vargo HoustonTX United States Dept. of Civil Construction and Environmental Engineering and Electrical and Computer Egineering Iowa State University AmesIA United States Departments of Mechanical Engineering and Electrical and Computer Engineering Iowa State University AmesIA United States

ISBN: (纸本)9780791884003

Several acquisition functions have been proposed to identify an optimal sequence of samples in sequential kriging-based reliability analysis. However, no single acquisition function provides better performance over the others in all cases. To address this problem, this paper proposes a new acquisition function, namely expected uncertainty reduction (EUR), that serves as a meta-criterion to select the best sample from a set of optimal samples, each identified from a large number of candidate samples according to the criterion of an acquisition function. EUR directly quantifies the expected reduction of the uncertainty in the prediction of limit-state function by adding an optimal sample. The uncertainty reduction is quantified by sampling over the kriging posterior. In the proposed EUR-based sequential sampling framework, a portfolio that consists of four acquisition functions is first employed to suggest four optimal samples at each iteration of sequential sampling. Then, EUR is employed as the meta-criterion to identify the best sample among those optimal samples. The results from two mathematical case studies show that (1) EUR-based sequential sampling can perform as well as or outperform the single use of any acquisition function in the portfolio, and (2) the best-performing acquisition function may change from one problem to another or even from one iteration to the next within a problem. © 2020 American Society of Mechanical Engineers (ASME). All rights reserved.

关键词： Reliability analysis

来源：评论

学校读者我要写书评

暂无评论

Special Section Guest Editorial: Advances in Breast Imaging

引用

Journal of Medical Imaging 2019年第3期6卷 031401页

作者： Krupinski, Elizabeth A. Astley, Sue Marti, Robert Tornai, Martin Zwiggelaar, Reyer Emory University Department of Radiology and Imaging Sciences AtlantaGA United States University of Manchester Division of Informatics Imaging and Data Science Manchester United Kingdom University of Girona Department of Architecture and Computer Technology Girona Spain Duke University Departments of Radiology and Biomedical Engineering DurhamNC United States Aberystwyth University Department of Computer Science Aberystwyth United Kingdom

This guest editorial introduces the special section on Advances in Breast Imaging.

关键词： Digital breast tomosynthesis Breast imaging Breast cancer Breast Cancer Digital mammography computer simulations Mammography Optical filters Radiology

来源：评论

学校读者我要写书评

暂无评论

SCALE RELIANT INFERENCE

arXiv

引用

arXiv 2022年

作者： Nixon, Michelle Pistner McGovern, Kyle C. Letourneau, Jeffrey David, Lawrence A. Lazar, Nicole A. Mukherjee, Sayan Silverman, Justin D. College of Information Sciences and Technology The Pennsylvania State University United States Program in Bioinformatics and Genomics The Pennsylvania State University United States Department of Molecular Genetics and Microbiology Duke University United States Department of Statistics The Pennsylvania State University United States Huck Institutes for the Life Sciences The Pennsylvania State University United States Departments of Statistical Science Mathematics Computer Science Biostatistics & Bioinformatics Duke University United States Center for Scalable Data Analytics and Artificial Intelligence University of Leipzig Germany Max Planck Institute for Mathematics in the Natural Sciences Germany Department of Medicine The Pennsylvania State University United States

Many scientific fields, including human gut microbiome science, collect multivariate count data where the sum of the counts is unrelated to the scale of the underlying system being measured (e.g., total microbial load in a subject’s colon). This disconnect complicates downstream analyses such as differential analysis in case-control studies. This article is motivated by a novel study of in vitro human gut microbiome models. Popular tools for analyzing these data led to dramatically elevated rates of both false positives and false negatives. To understand those failures, we provide a formal problem statement that frames these challenges of scale in terms of the classical theory of identifiability. We call this the problem of Scale Reliant Inference (SRI). We use this formulation to prove fundamental limits on SRI in terms of criteria such as consistency and type-I error control. We show that the failures of existing methods stem from a fundamental failure to properly quantify uncertainty in the system scale. We demonstrate that a particular type of Bayesian model called a Bayesian Partially Identified Model (PIMs) can correctly quantify uncertainty in SRI. We introduce Scale Simulation Random Variables (SSRVs) as a flexible and efficient approach to specifying and inferring Bayesian PIMs. In the context of both real and simulated data, we find SSRVs drastically decrease type-I and type-II error rates. © 2022, CC BY.

关键词： Bayesian networks

来源：评论

学校读者我要写书评

暂无评论

Artificial Intelligence for Public Health

Artificial Intelligence for Public Health

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Kristin P. Bennett Departments of Mathematical Science and Computer Science Institute for Data Application and Exploration Rensselaer Polytechnic Institute USA

In this talk, we examine artificial intelligence approaches for extracting actionable insights from health care data in order to improve public health. Our goal is to simultaneously identify subpopulations with distinct health risks and health trajectories and find the distinct risk factors or determinants associated each subpopulation. These determinants can then be used treatments, programs, and policies in order to reduce mortality and comorbidity and provide more efficient healthcare. We examine novel cadre machine learning approaches that combine predictive neural network modeling with more traditional statistical epidemiology methods for risk and survival analyses. We embed the cadre methods into a Semantically Targeted Analytics (Semantalytics) System that combines semantics, inference, automatic machine learning, and explainable AI. The AI system translate the public health questions to an analysis plan, prepares data, conducts analysis and reports results with visualization and text. We demonstrated these approach on public health care surveillance datasets and electronic medical records. The award winning “MortalityMinder” app examines the social determinants of “Deaths Despair” (deaths from suicide and substance abuse) and other causes of mortality that are unexpectedly rising in the United States. Other applications include association of environment toxins associated with diseases, high needs patient management for a health management organization, and emergency department readmissions. We conclude with the discussion of the open challenges to create population health AI systems that can transform health care questions into data-driven actionable-insights on the fly.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Multimodal Deep Learning for Pediatric Mild Traumatic Brain Injury Detection

Multimodal Deep Learning for Pediatric Mild Traumatic Brain ...

引用

IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Badhan Mazumder Deepan Krishna Tripathy Keith Owen Yeates Miriam H. Beauchamp William Craig Quynh Doan Stephen B. Freedman Catherine Lebel Roger Zemek Ashley L. Ware Dong Hye Ye Department of Computer Science Georgia State University Atlanta GA USA Department of Psychology University of Calgary Calgary Canada Department of Psychology University of Montreal and CHU Sainte-Justine Hospital Research Center Montreal Quebec Canada University of Alberta and Stollery Children’s Hospital Edmonton Canada Pediatric Emergency Medicine University of British Columbia Vancouver Canada Departments of Pediatrics and Emergency Medicine Cumming School of Medicine University of Calgary Calgary Canada Department of Radiology University of Calgary Calgary Canada Department of Pediatrics and Emergency Medicine University of Ottawa Children’s Hospital of Eastern Ontario Research Institute Ottawa Canada Department of Psychology Georgia State University Atlanta GA USA Department of Neurology University of Utah Salt Lake City Utah USA Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State Georgia Tech and Emory University

Despite its prevalence, little is known about the pathophysiology of mild traumatic brain injury (mTBI). This makes it difficult for clinicians to accurately diagnose mTBI and to predict outcomes in affected children, thereby highlighting the urgent need to identify novel and efficacious biomarkers of pediatric mTBI. To address this important knowledge gap, this study introduced a multimodal magnetic resonance imaging (MRI) based deep learning approach toward the classification of mTBI as compared with mild orthopedic injury (OI) by considering both structural MRI (sMRI) and diffusion tensor imaging (DTI). Firstly, convolutional features were extracted by employing a pre-trained DenseNet to capture the morphological features of both modalities. Next, by employing Deep Canonical Correlation Analysis (DCCA), distinct features obtained from the sMRI and DTI data were integrated into a multi-modal embedding. The obtained DCCA fused compact multimodal features were then fed to a random forest (RF) classifier that was used to classify mTBI versus mild OI. Additionally, to visualize the intra-individually heterogeneous brain regions that DenseNet most heavily relied upon for making classification, Gradient-weighted Class Activation Mapping (Grad-CAM) was applied to the DenseNet outcomes for both modalities. According to the experimental outcomes on the clinical dataset, the introduced multimodal deep learning strategy improved the classification accuracy by 8.6% (from 75.8% to 84.4%) and 7.8% (from 76.6% to 84.4%) when compared to the unimodal morphological features, as generated from sMRI and DTI.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Corrigendum to "A multimodal neuroimaging-based risk score for mild cognitive impairment" [NeuroImage: Clinical 45 (2025) 103719]

引用

NeuroImage. Clinical 2025年 45卷 103728页

作者： Elaheh Zendehrouh Mohammad S E Sendi Anees Abrol Ishaan Batta Reihaneh Hassanzadeh Vince D Calhoun Tri-institutional Center for Translational Research in Neuroimaging and Data Science Georgia State University Georgia Institute of Technology Emory University Atlanta GA United States Department of Electrical and Computer Engineering at Georgia Institute of Technology Atlanta GA United States. Electronic address: ezendehrouh3@gatech.edu. Harvard Medical School and McLean Hospital Boston MA United States. Tri-institutional Center for Translational Research in Neuroimaging and Data Science Georgia State University Georgia Institute of Technology Emory University Atlanta GA United States. Department of Electrical and Computer Engineering at Georgia Institute of Technology Atlanta GA United States. Department of Electrical and Computer Engineering at Georgia Institute of Technology Atlanta GA United States Departments of Psychology and Computer Science Georgia State University Atlanta GA United States. Electronic address: vcalhoun@gsu.edu.

来源：评论

学校读者我要写书评

暂无评论

A Generative Shape Compositional Framework to Synthesise Populations of Virtual Chimaeras

arXiv

引用

arXiv 2022年

作者： Dou, Haoran Virtanen, Seppo Ravikumar, Nishant Frangi, Alejandro F. The Center for Computational Imaging and Simulation Technologies in Biomedicine within the School of Computing The University of Leeds LeedsLS2 9JT United Kingdom The Christabel Pankhurst Institute Division of Informatics Imaging and Data Sciences University of Manchester ManchesterM1 3BB United Kingdom The Department of Computer Science University of Manchester ManchesterM1 3BB United Kingdom Departments of Electrical Engineering and Cardiovascular Sciences KU Leuven Leuven Belgium Alan Turing Institute London United Kingdom

Generating virtual organ populations that capture sufficient variability while remaining plausible is essential to conduct in-silico trials of medical devices. However, not all anatomical shapes of interest are always available for each individual in a population. The imaging examinations and modalities used can vary between subjects depending on their individualised clinical pathways. Different imaging modalities may have various fields of view, are sensitive to signals from other tissues/organs, or both. Hence, missing/partially overlapping anatomical information is often available across individuals. We introduce a generative shape model for multipart anatomical structures, learnable from sets of unpaired datasets, i.e., where each substructure in the shape assembly comes from datasets with missing or partially overlapping substructures from disjoint subjects of the same population. The proposed generative model can synthesise complete multipart shape assemblies coined virtual chimaeras. We applied this framework to build virtual chimaeras from databases of whole heart shape assemblies that each contribute samples for heart substructures. Specifically, we propose a graph neural network-based generative shape compositional framework which comprises two components, a part-aware generative shape model which captures the variability in shape observed for each structure of interest in the training population, and a spatial composition network which assembles/composes the structures synthesised by the former into multipart shape assemblies (viz. virtual chimaeras). We also propose a novel self-supervised learning scheme that enables the spatial composition network to be trained with partially overlapping data and weak labels. We trained and validated our approach using shapes of cardiac structures derived from cardiac magnetic resonance images in the UK Biobank. When trained with complete and partially overlapping data, our approach significantly outperforms a PCA-base

关键词： Magnetic resonance

来源：评论

学校读者我要写书评

暂无评论

Multidomain postoperative recovery trajectories after lumbar and thoracolumbar spine surgery

引用

Spine Journal 2025年

作者： Yakdan, Salim Zhang, Jingwen Benedict, Braeden Xu, Ziqi Javeed, Saad Zhang, Justin K. Steel, Benjamin A. Gupta, Vivek P. Botterbush, Kathleen Piccirillo, Jay F. Rodebaugh, Thomas L. Goodin, Burel R. Buchowski, Jacob M Neuman, Brian Hafez, Daniel Kelly, Michael Ray, Wilson Z. Lu, Chenyang Frumkin, Madelyn Greenberg, Jacob K. Department of Neurological Surgery Washington University School of Medicine St. Louis MO United States Department of Computer Science & Engineering Washington University in St. Louis St. Louis MO United States Department of Otolaryngology-Head and Neck Surgery Washington University School of Medicine St. Louis MO United States Department of Psychology and Neuroscience University of North Carolina at Chapel Hill Chapel Hill NC United States Department of Anesthesiology Washington University St. Louis MO United States Department of Orthopedic Surgery Washington University School of Medicine St. Louis MO United States Department of Orthopedic Surgery Rady Children's Hospital University of California San Diego CA United States AI for Health Institute Washington University St. Louis MO United States Center for Technology and Behavioral Health Departments of Biomedical Data Science and Psychiatry Geisel School of Medicine at Dartmouth Lebanon NH United States

BACKGROUND CONTEXT: Understanding early postoperative recovery is crucial for improving perioperative care and long-term outcomes. Traditional recovery assessments relying primarily on cross-sectional patient-reported measures may not fully capture the complexity of the recovery process. PURPOSE: Our study evaluates early postoperative recovery using mobile health assessment and explores the relationship between early recovery patterns, multidomain recovery, and long-term outcomes. STUDY DESIGN/SETTING: A prospective observational study. PATIENT SAMPLE: A total of 129 patients were included in the analysis. OUTCOME MEASURES: Patient-reported outcome measures (PROMs) of pain, physical function, and disability at 1, 6 and 12 months postoperatively. METHODS: This study recruited patients aged 21 to 85 undergoing lumbar/thoracolumbar surgery for degenerative diseases. During the first month postoperatively, patients completed daily Ecological Momentary Assessment (EMA) surveys assessing pain, depression, and physical function and were passively monitored with Fitbit. EMA and Fitbit data (steps per minute, Maximum 30-minute cadence, and activity bout number) were clustered using functional principal component analysis. RESULTS: 129 patients were included (median age: 62;56% female). Two dominant recovery patterns were identified across all domains. At 12 months, patients with more favorable recovery across all domains except for activity bout number and depression, showed better outcomes. Favorable recovery in pain intensity and steps per minute was associated with greater improvements in all outcomes, while favorable recovery in maximum 30-minute cadence was associated with greater improvement in disability and physical function. Patients with less favorable pain recovery had a significantly higher complication rate (23% vs. 7%). A greater number of favorable recovery domains was associated with better 12-month outcomes improvement and demonstrated superior prognostic v

关键词： Degenerative spine disease Disability Ecological momentary assessment Fitbit Long-term outcomes Physical function Spine surgery

来源：评论

学校读者我要写书评

暂无评论

Towards Quantification of Bias in Machine Learning for Healthcare: A Case Study of Renal Failure Prediction

arXiv

引用

arXiv 2019年

作者： Williams, Josie V. Razavian, Narges Departments of Computer Science Courant Institute of Mathematical Sciences New York University Departments of Population Health Radiology Center for Data Science New York University Langone Medical Center

As machine learning (ML) models, trained on real-world datasets, become common practice, it is critical to measure and quantify their potential biases. In this paper, we focus on renal failure and compare a commonly used traditional risk score, Tangri, with a more powerful machine learning model, which has access to a larger variable set and trained on 1.6 million patients’ EHR data. We will compare and discuss the generalization and applicability of these two models, in an attempt to quantify biases of status quo clinical practice, compared to ML-driven models. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

arXiv

引用

arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

来源：评论

学校读者我要写书评

暂无评论

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

请选择保存的检索档案：

请选择收藏分类：

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

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

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：