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Comprehensive prediction of outcomes in patients with ST elevation myocardial infarction (STEMI) using tree-based machine learning algorithms

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computers in Biology and Medicine 2025年 184卷 109439-109439页

作者： Razavi, Seyed Reza Zaremba, Alexander C. Szun, Tyler Cheung, Seth Shah, Ashish H. Moussavi, Zahra Biomedical Engineering Program University of Manitoba WinnipegMBR3T 5V6 Canada Department of Medicine Rady Faculty of Health Sciences University of Manitoba WinnipegMBR3E 3P5 Canada St Boniface Hospital University of Manitoba WinnipegMBR2H 2A6 Canada Department of Electrical and Computer Engineering University of Manitoba WinnipegMBR3T 5V6 Canada

ST elevation myocardial infarction (STEMI), a subtype of acute coronary syndrome, is one of the leading causes of morbidity and mortality. Revascularization using primary percutaneous coronary intervention (PPCI) is the gold standard treatment. Despite the restoration of myocardial blood flow, some patients experience adverse outcomes. Early detection of high-risk patients would facilitate timely management, potentially improving their morbidity, mortality, and quality of life. In-depth characterization of the aortic pressure (AP) waveform may identify a high-risk patient cohort. We present tree-based classifiers and features extracted from the AP signals to identify patients at risk of adverse outcomes. This is a single-center, retrospective cohort study that included 605 eligible STEMI patients [64.2 ± 13.2 years, 71.4 % (432) males] treated with PPCI. Outcomes, including mortality (within 30-day and 1-year), and in-hospital events such as prolonged in-hospital stay (>4 days) for medical reasons, a new diagnosis of heart failure (HF), diuretic use for more than 24 h, intubation-ventilation or BiPAP use, and inotropic and/or vasopressor use, were recorded. We extracted features mainly from denoised AP signals recorded during PPCI, followed by different feature selection algorithms and classification methods to predict outcomes. Various classifiers such as tree-based classifiers, including random forest (RF), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and CatBoost, were used. Using recursive feature elimination (RFE) as the feature selection method and the CatBoost classifier, we achieved a receiver operating characteristic curve's area under the curve (AUC) of 80 % for all outcomes except for the new diagnosis of HF and diuretic use (>24 h). For the new diagnosis of HF and diuretic use (>24 h), the AUC values were 73 % and 79 %, respectively. In conclusion, tree-based classifiers using features extracted from AP traces can effectively identi

关键词： Adaptive boosting

Immersive Modeling Framework for Training Applications

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Immersive Modeling Framework for Training Applications

International Conference on Advanced Learning Technologies (ICALT)

作者： Vinicius Chrisosthemos Teixeira Carlo Smaniotto Mantovani Alexandre Cardoso Carlos Alexandre dos Santos Márcio Sarroglia Pinho Computer Science Graduate Program School of Technology Pontifical Catholic University of Rio Grande do Sul Porto Alegre Brazil Faculty of Electrical Engineering Federal University of Uberlândia Uberlândia Brazil School of Technology Pontifical Catholic University of Rio Grande do Sul Porto Alegre Brazil

This article describes a framework for modeling and executing training in augmented and virtual reality environments. The framework was designed based on characteristics observed in existing training applications for complex tasks, such as the use of tools, control panels and the need for step-by-step instructions. Unlike other frameworks, in the proposed system, it is possible to create the training entirely within the virtual/augmented environment, avoiding constant switching between 2D and 3D environments. The framework allows for the definition of steps in a training program, each of which includes textual instructions, videos, and 3D objects, static or animated, anchored in the real world. To demonstrate the capabilities of the framework, a training program for operating a Universal Testing Machine was created as a case study. Overall, the proposed framework allows for the creation of effective and efficient AR training programs for a variety of tasks and industries.

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Low power In Memory Computation with Reciprocal Ferromagnet/Topological Insulator Heterostructures

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arXiv 2022年

作者： Vakili, Hamed Ganguly, Samiran de Coster, George J. Neupane, Mahesh R. Ghosh, Avik W. Department of Physics University of Virginia CharlottesvilleVA22904 United States Department of Electrical and Computer Engineering University of Virginia CharlottesvilleVA22904 United States Department of Electrical and Computer Engineering Virginia Commonwealth University RichmondVA23284 United States DEVCOM Army Research Laboratory 2800 Powder Mill Rd AdelphiMD20783 United States Materials Science and Engineering Program University of California RiversideCA92521 United States

The surface state of a 3D topological insulator (3DTI) is a spin-momentum locked conductive state, whose large spin hall angle can be used for the energy-efficient spin orbit torque based switching of an overlying ferromagnet (FM). Conversely, the gated switching of the magnetization of a separate FM in or out of the TI surface plane, can turn on and off the TI surface current. By exploiting this reciprocal behaviour, we can use two FM/3DTI heterostructures to design an integrated 1-Transistor 1-magnetic tunnel junction random access memory unit (1T1MTJ RAM) for an ultra low power Processing-in-Memory (PiM) architecture. Our calculation involves combining the Fokker-Planck equation with the Non-equilibrium Green Function (NEGF) based flow of conduction electrons and Landau-Lifshitz-Gilbert (LLG) based dynamics of magnetization. Our combined approach allows us to connect device performance metrics with underlying material parameters, which can guide proposed experimental and fabrication efforts. © 2022, CC BY-NC-SA.

关键词： Magnetization

Transmission switching under wind uncertainty using linear decision rules

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Transmission switching under wind uncertainty using linear d...

2020 IEEE Power and Energy Society General Meeting, PESGM 2020

作者： Zhou, Yuqi Zhu, Hao Hanasusanto, Grani A. The University of Texas at Austin Department of Electrical and Computer Engineering AustinTX United States The University of Texas at Austin Graduate Program in Operations Research Industrial Engineering AustinTX United States

ISBN: (纸本)9781728155081

Increasing penetration of wind and renewable generation poses significant challenges to the power system operations and reliability. This paper considers the real-time optimal transmission switching (OTS) problem for determining the generation dispatch and network topology that can account for uncertain energy resources. To efficiently solve the resultant two-stage stochastic program, we propose a tractable linear decision rule (LDR) based approximation solution that can eliminate the uncertain variables and lead to fixed number of constraints. The proposed LDR approach can guarantee feasibility, and significantly reduces the computational complexity of existing approaches that grows with the number of randomly generated samples of uncertainty. Numerical studies on IEEE test cases demonstrate the high approximation accuracy of the proposed LDR solution and its computational efficiency for real-time OTS implementations. © 2020 IEEE.

关键词： Energy resources

Unsupervised Domain Adaptation for Segmentation with Black-box Source Model

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arXiv 2022年

作者： Liu, Xiaofeng Yoo, Chaehwa Xing, Fangxu Kuo, C.-C. Jay Fakhri, Georges El Kang, Je-Won Woo, Jonghye Gordon Center for Medical Imaging Massachusetts General Hospital Harvard Medical School BostonMA United States Dept. of Electronic and Electrical Engineering and Graduate Program in Smart Factory Ewha Womans University Seoul Korea Republic of Dept. of Electrical and Computer Engineering University of Southern California Los AngelesCA United States

Unsupervised domain adaptation (UDA) has been widely used to transfer knowledge from a labeled source domain to an unlabeled target domain to counter the difficulty of labeling in a new domain. The training of conventional solutions usually relies on the existence of both source and target domain data. However, privacy of the large-scale and well-labeled data in the source domain and trained model parameters can become the major concern of cross center/domain collaborations. In this work, to address this, we propose a practical solution to UDA for segmentation with a black-box segmentation model trained in the source domain only, rather than original source data or a white-box source model. Specifically, we resort to a knowledge distillation scheme with exponential mixup decay (EMD) to gradually learn target-specific representations. In addition, unsupervised entropy minimization is further applied to regularization of the target domain confidence. We evaluated our framework on the BraTS 2018 database, achieving performance on par with white-box source model adaptation approaches. © 2022, CC BY.

关键词： Distillation

Representing and extending ensembles of parsimonious evolutionary histories with a directed acyclic graph

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arXiv 2023年

作者： Dumm, Will Barker, Mary Howard-Snyder, William DeWitt, William S. Matsen, Frederick A. Computational Biology Program Fred Hutchinson Cancer Research Center SeattleWA United States Howard Hughes Medical Institute Computational Biology Program Fred Hutchinson Cancer Research Center SeattleWA United States Paul G. Allen School of Computer Science and Engineering University of Washington SeattleWA United States Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA United States Department of Genome Sciences University of Washington SeattleWA United States Department of Statistics University of Washington SeattleWA United States

In many situations, it would be useful to know not just the best phylogenetic tree for a given data set, but the collection of high-quality trees. This goal is typically addressed using Bayesian techniques, however, current Bayesian methods do not scale to large data sets. Furthermore, for large data sets with relatively low signal one cannot even store every good tree individually, especially when the trees are required to be bifurcating. In this paper, we develop a novel object called the "history subpartition directed acyclic graph" (or "history sDAG" for short) that compactly represents an ensemble of trees with labels (e.g. ancestral sequences) mapped onto the internal nodes. The history sDAG can be built efficiently and can also be efficiently trimmed to only represent maximally parsimonious trees. We show that the history sDAG allows us to find many additional equally parsimonious trees, extending combinatorially beyond the ensemble used to construct it. We argue that this object could be useful as the "skeleton" of a more complete uncertainty *** Codes 92-08 (Primary) 92B10, 92-04 (Secondary) Copyright © 2023, The Authors. All rights reserved.

关键词： Phylogenetic

Quality Improvements in the electrical Design of Typical DC-DC Electronic Power Converter for High-Voltage Applications

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Quality Improvements in the Electrical Design of Typical DC-...

International Conference on engineering and Emerging Technologies (ICEET)

作者： King Harold A. Recto Miguel T. Escoto Noel B. Hernandez Department of Electronics Computer and Communications Engineering Ateneo de Manila University Quezon City Philippines Electrical and Electronics Engineering Institute University of the Philippines Diliman Quezon City Philippines Post-doctoral Program in Quality Management of the Graduate School Centro Escolar University Manila Philippines

ISBN: (纸本)9781665491075

The green transition has brought about a worldwide-shift to the use of renewables as alternative energy sources. Because of this, high voltage DC has been a field of interest in power electronics due to its capability in efficiently transmitting power to reach remote areas. Typical DC-DC electronic power converter design has the capability of boosting voltage level to a certain extent but not enough for high-voltage applications. Using the DMAIC Approach of the Six Sigma, quality improvements in the design was proposed to stabilize operating frequency, bring up the output voltage from 100 VDC to 4,000 VDC and provide at least 10% margin of error in the apparent power measured at the output side to offset inherent losses in the electrical design. These proposed quality improvements were sustained by designing a control circuit that would ensure the attainment of the parameters set in the Define Phase of the DMAIC Approach. The result of the study is a practical 12 VDC to 4,000 VDC boost converter design that has incorporated all the proposed quality improvements.

关键词： Renewable energy sources Voltage measurement Power measurement Measurement uncertainty Green products High-voltage techniques Loss measurement

Long-range photodetection of organic exciton-polaritons

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Long-range photodetection of organic exciton-polaritons

Conference on Lasers and Electro-Optics (CLEO)

作者： Bin Liu Xinjing Huang Stephen R. Forrest Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor USA Applied Physics Program University of Michigan Ann Arbor USA Department of Physics University of Michigan Ann Arbor USA Department of Material Science and Engineering University of Michigan Ann Arbor USA

In light-matter strong coupling regime, we observe long-range photodetection response at room temperature mediated by organic exciton-polaritons, which results from strong interactions between organic excitons and low... 详细信息

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

关键词： Couplings Surface waves Excitons Lasers and electrooptics Laser excitation Electrooptical waveguides

Scalable privacy-preserving cancer type prediction with homomorphic encryption

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arXiv 2022年

作者： Sarkar, Esha Chielle, Eduardo Gursoy, Gamze Chen, Leo Gerstein, Mark Maniatakos, Michail Tandon School of Engineering New York University Brooklyn United States Department of Electrical and Computer Engineering New York University Abu Dhabi Abu Dhabi United Arab Emirates Program in Computational Biology and Bioinformatics Yale University New Haven United States Department of Computer Science Yale University New Haven United States

Machine Learning (ML) alleviates the challenges of high-dimensional data analysis and improves decision making in critical applications like healthcare. Effective cancer/tumor classification from high-dimensional genetic mutation data can be useful for cancer diagnosis and treatment, if the distinguishable patterns between cancer/tumor types are identified. At the same time, analysis of high-dimensional data is computationally expensive and is often outsourced to cloud services. Privacy concerns in outsourced ML, especially in the field of genetics, motivate the use of encrypted computation, like Homomorphic Encryption (HE). But restrictive overheads of encrypted computation deter its usage. In this work, we explore the challenges of privacy preserving cancer detection using a real-world dataset consisting of more than 2 million genetic information for several cancer types. Since the data is inherently high-dimensional, we explore smaller ML models for cancer prediction to enable fast inference in the privacy preserving domain. We develop a solution for privacy preserving cancer inference which first leverages the domain knowledge on somatic mutations to efficiently encode genetic mutations and then uses statistical tests for feature selection. Our logistic regression model, built using our novel encoding scheme, achieves 0.98 micro-average area under curve with 13% higher test accuracy than similar studies. We exhaustively test our model’s predictive capabilities by analyzing the genes used by the model. Furthermore, we propose a fast matrix multiplication algorithm that can efficiently handle high-dimensional data. Experimental results show that, even with 40,000 features, our proposed matrix multiplication algorithm can speed up concurrent inference of multiple individuals by approximately 10x and inference of a single individual by approximately 550x, in comparison to standard matrix multiplication. © 2022, CC BY.

关键词： Clustering algorithms