检索结果-内蒙古大学图书馆

Dynamic prediction of mortality in COVID-19 patients in the intensive care unit: A retrospective multi-center cohort study

引用

Intelligence-Based Medicine 2022年 6卷 100071-100071页

作者： Smit, J.M. Krijthe, J.H. Endeman, H. Tintu, A.N. de Rijke, Y.B. Gommers, D.A.M.P.J. Cremer, O.L. Bosman, R.J. Rigter, S. Wils, E.-J. Frenzel, T. Dongelmans, D.A. De Jong, R. Peters, M.A.A. Kamps, M.J.A. Ramnarain, D. Nowitzky, R. Nooteboom, F.G.C.A. De Ruijter, W. Urlings-Strop, L.C. Smit, E.G.M. Mehagnoul-Schipper, D.J. Dormans, T. De Jager, C.P.C. Hendriks, S.H.A. Achterberg, S. Oostdijk, E. Reidinga, A.C. Festen-Spanjer, B. Brunnekreef, G.B. Cornet, A.D. Van den Tempel, W. Boelens, A.D. Koetsier, P. Lens, J.A. Faber, H.J. karakus, A. Entjes, R. De Jong, P. Rettig, T.C.D. Arbous, M.S. Lalisang, R.C.A. Tonutti, M. De Bruin, D.P. Elbers, P.W.G. Van Bommel, J. Reinders, M.J.T. Intensive Care Erasmus University Medical Center Rotterdam Netherlands Pattern Recognition & Bioinformatics Group EEMCS Delft University of Technology Delft Netherlands Clinical Chemistry Erasmus University Medical Center Rotterdam Netherlands Intensive Care UMC Utrecht Utrecht Netherlands Intensive Care OLVG Amsterdam Netherlands Anesthesiology and Intensive Care St. Antonius Hospital Nieuwegein Netherlands Intensive Care Franciscus Gasthuis Vlietland Rotterdam Netherlands Intensive Care Radboud University Medical Center Nijmegen Netherlands Intensive Care Amsterdam UMC Amsterdam Netherlands Intensive Care Bovenij Ziekenhuis Amsterdam Netherlands Intensive Care Canisius Wilhelmina Ziekenhuis Nijmegen Netherlands Intensive Care Catharina Ziekenhuis Eindhoven Eindhoven Netherlands Intensive Care ETZ Tilburg Tilburg Netherlands Intensive Care HagaZiekenhuis Den Haag Netherlands Intensive Care Laurentius Ziekenhuis Roermond Netherlands Intensive Care Northwest Clinics Alkmaar Netherlands Intensive Care Reinier de Graaf Gasthuis Delft Netherlands Intensive Care Spaarne Gasthuis Haarlem en Hoofddorp Netherlands Intensive Care VieCuri Medisch Centrum Venlo Netherlands Intensive Care Zuyderland MC Heerlen Netherlands Intensive Care Jeroen Bosch Ziekenhuis 's-Hertogenbosch Netherlands Intensive Care Albert Schweitzerziekenhuis Dordrecht Netherlands Intensive Care Haaglanden Medisch Centrum Den Haag Netherlands Intensive Care Maasstad Ziekenhuis Rotterdam Rotterdam Netherlands Intensive Care SEH BWC Martiniziekenhuis Groningen Netherlands Intensive Care Ziekenhuis Gelderse Vallei Ede Netherlands Intensive Care Ziekenhuisgroep Twente Almelo Netherlands Intensive Care Medisch Spectrum Twente Enschede Netherlands Intensive Care Ikazia Ziekenhuis Rotterdam Rotterdam Netherlands Anesthesiology Antonius Ziekenhuis Sneek Sneek Netherlands Intensive Care Medisch Centrum Leeuwarden Leeuwarden Netherlands Intensive Care IJsselland Zieke

Background: The COVID-19 pandemic continues to overwhelm intensive care units (ICUs) worldwide, and improved prediction of mortality among COVID-19 patients could assist decision making in the ICU setting. In this work, we report on the development and validation of a dynamic mortality model specifically for critically ill COVID-19 patients and discuss its potential utility in the ICU. Methods: We collected electronic medical record (EMR) data from 3222 ICU admissions with a COVID-19 infection from 25 different ICUs in the Netherlands. We extracted daily observations of each patient and fitted both a linear (logistic regression) and non-linear (random forest) model to predict mortality within 24 h from the moment of prediction. Isotonic regression was used to re-calibrate the predictions of the fitted models. We evaluated the models in a leave-one-ICU-out (LOIO) cross-validation procedure. Results: The logistic regression and random forest model yielded an area under the receiver operating characteristic curve of 0.87 [0.85;0.88] and 0.86 [0.84;0.88], respectively. The recalibrated model predictions showed a calibration intercept of −0.04 [−0.12;0.04] and slope of 0.90 [0.85;0.95] for logistic regression model and a calibration intercept of −0.19 [−0.27;−0.10] and slope of 0.89 [0.84;0.94] for the random forest model. Discussion: We presented a model for dynamic mortality prediction, specifically for critically ill COVID-19 patients, which predicts near-term mortality rather than in-ICU mortality. The potential clinical utility of dynamic mortality models such as benchmarking, improving resource allocation and informing family members, as well as the development of models with more causal structure, should be topics for future research. © 2022 The Authors

关键词：

来源：评论

学校读者我要写书评

暂无评论

Correction to: Gene expression identifies patients who develop inflammatory arthritis in a clinically suspect arthralgia cohort

引用

Arthritis research & therapy 2020年第1期22卷 289页

作者： Ellis Niemantsverdriet Erik B van den Akker Debbie M Boeters Susan J F van den Eeden Annemieke Geluk Annette H M van der Helm-van Mil Department of Rheumatology Leiden University Medical Center PO Box 9600 Leiden 2300 RC The Netherlands. e.niemantsverdriet@lumc.nl. Department of Biomedical Data Sciences Leiden University Medical Center Leiden The Netherlands. Pattern Recognition & Bioinformatics Delft University of Technology Delft The Netherlands. Department of Rheumatology Leiden University Medical Center PO Box 9600 Leiden 2300 RC The Netherlands. Department of Infectious Diseases/Immunohematology and Blood Transfusion Leiden University Medical Center Leiden The Netherlands. Department of Rheumatology Erasmus Medical Center Rotterdam The Netherlands.

An amendment to this paper has been published and can be accessed via the original article.

关键词：

来源：评论

学校读者我要写书评

暂无评论

What Will Your Future Child Look Like? Modeling and Synthesis of Hereditary patterns of Facial Dynamics

What Will Your Future Child Look Like? Modeling and Synthesi...

引用

International Conference on Automatic Face and Gesture recognition

作者： Itir Önal Ertugrul Hamdi Dibeklioglu Department of Computer Engineering Middle East Technical University Ankara Turkey Pattern Recognition & Bioinformatics Group Delft University of Technology Delft The Netherlands

Analysis of kinship from facial images or videos is an important problem. Prior machine learning and computer vision studies approach kinship analysis as a verification or recognition task. In this paper, first time in the literature, we propose a kinship synthesis framework, which generates smile videos of (probable) children from the smile videos of parents. While the appearance of a child's smile is learned using a convolutional encoder-decoder network, another neural network models the dynamics of the corresponding smile. The smile video of the estimated child is synthesized by the combined use of appearance and dynamics models. In order to validate our results, we perform kinship verification experiments using videos of real parents and estimated children generated by our framework. The results show that generated videos of children achieve higher correct verification rates than those of real children. Our results also indicate that the use of generated videos together with the real ones in the training of kinship verification models, increases the accuracy, suggesting that such videos can be used as a synthetic dataset.

关键词： Videos Face Neural networks Eyebrows Nose Mouth Time series analysis

来源：评论

学校读者我要写书评

暂无评论

Nuclear discrepancy for active learning

arXiv

引用

arXiv 2017年

作者： Viering, Tom J. Krijthe, Jesse H. Loog, Marco Pattern Recognition Laboratory Delft University of Technology Delft Netherlands Data Science Group Radboud University Nijmegen Pattern Recognition Laboratory Delft University of Technology University of Copenhagen Copenhagen Denmark

Active learning algorithms propose which unlabeled objects should be queried for their labels to improve a predictive model the most. We study active learners that minimize generalization bounds and uncover relationships between these bounds that lead to an improved approach to active learning. In particular we show the relation between the bound of the state-of-the-art Maximum Mean Discrepancy (MMD) active learner, the bound of the Discrepancy, and a new and looser bound that we refer to as the Nuclear Discrepancy bound. We motivate this bound by a probabilistic argument: we show it considers situations which are more likely to occur. Our experiments indicate that active learning using the tightest Discrepancy bound performs the worst in terms of the squared loss. Overall, our proposed loosest Nuclear Discrepancy generalization bound performs the best. We confirm our probabilistic argument empirically: the other bounds focus on more pessimistic scenarios that are rarer in practice. We conclude that tightness of bounds is not always of main importance and that active learning methods should concentrate on realistic scenarios in order to improve performance. Copyright © 2017, 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

Robust Gram embeddings

Robust Gram embeddings

引用

2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016

作者： Kekeç, Taygun Tax, D.M.J. Pattern Recognition and Bioinformatics Laboratory Delft University of Technology Delft2628CD Netherlands

ISBN: (纸本)9781945626258

Word embedding models learn vectorial word representations that can be used in a variety of NLP applications. When training data is scarce, these models risk losing their generalization abilities due to the complexity of the models and the overfitting to finite data. We propose a regularized embedding formulation, called Robust Gram (RG), which penalizes overfitting by suppressing the disparity between target and context embeddings. Our experimental analysis shows that the RG model trained on small datasets generalizes better compared to alternatives, is more robust to variations in the training set, and correlates well to human similarities in a set of word similarity tasks. © 2016 Association for Computational Linguistics

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Dynamic vortical flow profile quantitatively characterizes mitral valvular-left ventricular hemodynamic coupling: In vivo analysis by dynamic 3D enstrophy mapping from 4D Flow MRI

arXiv

引用

arXiv 2018年

作者： Elbaz, Mohammed S.M. Le, Trung Bao Garg, Pankaj Roest, Arno A.W. Lelieveldt, Boudewijn P.F. Plein, Sven Westenberg, Jos J.M. Sotiropoulos, Fotis van der Geest, Rob J. Department of Radiology Northwestern University ChicagoIL United States Department of Radiology Leiden University Medical Center Leiden Netherlands Department of Civil Engineering Stony Brook University Stony BrookNY United States University of Leeds Leeds United Kingdom Department of Pediatrics Division of Pediatric Cardiology Leiden University Medical Center Leiden Netherlands Pattern Recognition and Bioinformatics Group Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Computational Fluids Laboratory North Dakota State University United States

Vortical blood flow in the human left ventricular (LV) inflow initiates from the mitral valve (MV) and evolves within the LV during diastolic E-filling. Hence, vortical flow links MV and LV hemodynamics. This study sought to elucidate and quantitatively characterize the in vivo 3D dynamics of LV vortical flow over E-filling and relation to MV-LV hemodynamic coupling using 4D Flow MRI flow field. 34 healthy volunteers and 5 example patients underwent 4D Flow MRI. Vortical blood flow evolution was mapped in the LV over E-wave using enstrophy density. A new dimensionless profile PMV-LV was derived as a function of both MV vortex formation time (VFT) and LV volumetric enstrophy density. Results reveal that 3D vortical flow evolution in the healthy LV follows a bi-phasic behavior with a vortical growth phase followed by a vortical decay phase. In healthy LVs studied, the PMV-LV profile showed that the vortical growth and decay phases are characterized by a vortical growth time Tgrowth = 1.23 ± 0.25, growth rate α = 0.80 ± 0.17, decay time Tdecay = 0.96 ± 0.39 and decay rate β = -1.02 ± 0.49. Distinctly altered parameters were found in the pilot patients studied. The derived PMV-LV profile quantitatively characterizes MV-LV hemodynamic coupling by vortical flow dynamics. Results herein unravel new insights into cardiac physiology and could enable a novel standardized methodology to study MV-LV hemodynamic coupling and association to cardiac function in health and disease. Copyright © 2018, The Authors. All rights reserved.

关键词： Heart

来源：评论

学校读者我要写书评

暂无评论

Supervised Classification: Quite a Brief Overview

arXiv

引用

arXiv 2017年

作者： Loog, Marco Pattern Recognition Laboratory Delft University of Technology Netherlands Image Section University of Copenhagen Denmark

The original problem of supervised classification considers the task of automatically assigning objects to their respective classes on the basis of numerical measurements derived from these objects. Classifiers are the tools that implement the actual functional mapping from these measurements—also called features or inputs—to the so-called class label—or output. The fields of pattern recognition and machine learning study ways of constructing such classifiers. The main idea behind supervised methods is that of learning from examples: given a number of example input-output relations, to what extent can the general mapping be learned that takes any new and unseen feature vector to its correct class? This chapter provides a basic introduction to the underlying ideas of how to come to a supervised classification problem. In addition, it provides an overview of some specific classification techniques, delves into the issues of object representation and classifier evaluation, and (very) briefly covers some variations on the basic supervised classification task that may also be of interest to the practitioner. Copyright © 2017, The Authors. All rights reserved.

关键词： Supervised learning

来源：评论

学校读者我要写书评

暂无评论

Scale-regularized filter learning: Calculus of variation meets learning

arXiv

引用

arXiv 2017年

作者： Loog, Marco Lauze, François Pattern Recognition Laboratory Delft University of Technology Netherlands Image Section University of Copenhagen Denmark

We start out by demonstrating that an elementary learning task, corresponding to the training of a single linear neuron in a convolutional neural network, can be solved for feature spaces of very high dimensionality. In a second step, acknowledging that such high-dimensional learning tasks typically benefit from some form of regularization and arguing that the problem of scale has not been taken care of in a very satisfactory manner, we come to a combined resolution of both of these shortcomings by proposing a form of scale regularization. Moreover, using variational method, this regularization problem can also be solved rather efficiently and we demonstrate, on an artificial filter learning problem, the capabilities of our basic linear neuron. From a more general standpoint, we see this work as prime example of how learning and variational methods could, or even should work to their mutual benefit. Copyright © 2017, The Authors. All rights reserved.

关键词： Ordinary differential equations

来源：评论

学校读者我要写书评

暂无评论

A variance maximization criterion for active learning

arXiv

引用

arXiv 2017年

作者： Yang, Yazhou Loog, Marco Pattern Recognition Laboratory Delft University of Technology Van Mourik Broekmanweg 6 Delft2628 XE Netherlands

Active learning aims to train a classifier as fast as possible with as few labels as possible. The core element in virtually any active learning strategy is the criterion that measures the usefulness of the unlabeled data based on which new points to be labeled are picked. We propose a novel approach which we refer to as maximizing variance for active learning or MVAL for short. MVAL measures the value of unlabeled instances by evaluating the rate of change of output variables caused by changes in the next sample to be queried and its potential labelling. In a sense, this criterion measures how unstable the classifier's output is for the unlabeled data points under perturbations of the training data. MVAL maintains, what we refer to as, retraining information matrices to keep track of these output scores and exploits two kinds of variance to measure the informativeness and representativeness, respectively. By fusing these variances, MVAL is able to select the instances which are both informative and representative. We employ our technique both in combination with logistic regression and support vector machines and demonstrate that MVAL achieves state-of-the-art performance in experiments on a large number of standard benchmark datasets. Copyright © 2017, The Authors. All rights reserved.

关键词： Benchmarking

来源：评论

学校读者我要写书评

暂无评论

Active learning using uncertainty information

arXiv

引用

arXiv 2017年

作者： Yang, Yazhou Yang, Yazhou Loog, Marco Loog, Marco Pattern Recognition Laboratory Delft University of Technology Delft Netherlands College of Information System and Management National University of Defense Technology Changsha China Image Section University of Copenhagen Copenhagen Denmark

Many active learning methods belong to the retraining-based approaches, which select one unlabeled instance, add it to the training set with its possible labels, retrain the classification model, and evaluate the criteria that we base our selection on. However, since the true label of the selected instance is unknown, these methods resort to calculating the average-case or worse-case performance with respect to the unknown label. In this paper, we propose a different method to solve this problem. In particular, our method aims to make use of the uncertainty information to enhance the performance of retraining-based models. We apply our method to two state-of-the-art algorithms and carry out extensive experiments on a wide variety of realworld datasets. The results clearly demonstrate the effectiveness of the proposed method and indicate it can reduce human labeling efforts in many real-life applications. Copyright © 2017, The Authors. All rights reserved.

关键词： Classification (of information)

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：