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Development,validation,and transportability of several machine-learned,non-exercise-based VO_(2max)prediction models for older adults

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Journal of Sport and Health science 2024年第5期13卷 611-620页

作者： Benjamin T.Schumacher Michael J.LaMonte Andrea Z.LaCroix Eleanor M.Simonsick Steven P.Hooker Humberto Parada Jr. John Bellettiere Arun Kumar Herbert Wertheim School of Public Health and Human Longevity Science University of California San DiegoLa JollaCA 92093USA Department of Epidemiology and Environmental Health School of Public Health and Health ProfessionsUniversity at BuffaloState University of New YorkBuffaloNY 14214USA Translational Gerontology Branch Intramural Research ProgramNational Institute on AgingNational Institutes of HealthBaltimoreMD 21225USA College of Health and Human Services San Diego State UniversitySan DiegoCA 92182USA Division of Epidemiology and Biostatistics School of Public HealthSan Diego State UniversitySan DiegoCA 92182USA University of California San Diego Moores Cancer Center La JollaCA 92093USA Computer Science and Engineering and Halicioglu Data Science Institute University of California San DiegoLa JollaCA 92093USA

Background:There exist few maximal oxygen uptake(VO_(2max))non-exercise-based prediction equations,fewer using machine learning(ML),and none specifically for older *** direct measurement of VO_(2max)is infeasible in large epidemiologic cohort studies,we sought to develop,validate,compare,and assess the transportability of several ML VO_(2max)prediction ***:The Baltimore Longitudinal Study of Aging(BLSA)participants with valid VO2_(max)tests were included(n=1080).Least absolute shrinkage and selection operator,linear-and tree-boosted extreme gradient boosting,random forest,and support vector machine(SVM)algorithms were trained to predict VO_(2max)*** developed these algorithms for:(a)the overall BLSA,(b)by sex,(c)using all BLSA variables,and(d)variables common in aging ***,we quantified the associations between measured and predicted VO_(2max)and ***:The age was 69.0±10.4 years(mean±SD)and the measured VO_(2max)was 21.6±5.9 mL/kg/*** absolute shrinkage and selection operator,linear-and tree-boosted extreme gradient boosting,random forest,and support vector machine yielded root mean squared errors of 3.4 mL/kg/min,3.6 mL/kg/min,3.4 mL/kg/min,3.6 mL/kg/min,and 3.5 mL/kg/min,*** quartiles of measured VO_(2max)showed an inverse gradient in mortality *** VO_(2max)variables yielded similar effect estimates but were not robust to ***:Measured VO_(2max)is a strong predictor of *** ML can improve the accuracy of prediction as compared to simpler approaches but estimates of association with mortality remain sensitive to *** studies should seek to reproduce these results so that VO_(2max),an important vital sign,can be more broadly studied as a modifiable target for promoting functional resiliency and healthy aging.

关键词： Cardiorespiratory fitness Prediction algorithms Epidemiology Mortality

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质谱成像中的计算策略综述

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分析科学学报 2019年第4期35卷 497-506页

作者：许光甘胜丰李建军杨莉湖北第二师范学院计算机学院湖北武汉430205 Department of Computer Science Texas A&M University Corpus ChristiTXUSA 78412 Human Health Therapeutics National Research Council CanadaOttawaOntarioCanada K1A 0R6

质谱成像(MSI)已广泛应用于蛋白质组学、代谢组学、脂质组学和糖组学,以及与组织医学诊断和临床应用相关的生物医学研究领域。质谱成像能提供生物组织样本中空间分子组成的信息。由于MSI数据量异常庞大,人工数据解析和可视化分析的低效... 详细信息

质谱成像(MSI)已广泛应用于蛋白质组学、代谢组学、脂质组学和糖组学,以及与组织医学诊断和临床应用相关的生物医学研究领域。质谱成像能提供生物组织样本中空间分子组成的信息。由于MSI数据量异常庞大,人工数据解析和可视化分析的低效率、低精度已成为了研究中的瓶颈。因此,近年来在MSI的相关研究中出现了各种计算算法协助数据解析。本文综述了MSI最常用的计算策略及其在生物医学研究中的应用,涵盖了MSI数据预处理、降维、聚类、分类等数据分析方法和MSI软件工具包的最新研究成果。

关键词：质谱成像机器学习分类聚类 MSI软件

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An Information and Predictive Credibility Control System Using Blockchain Technique

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Journal of Physics: Conference Series 2021年第4期1964卷

作者： L Shakkeera S S Arumugam J Jegan Amarnath A Narasima Venkatesh Department of Computer Science and Engineering VIT Bhopal University Bhopal Madhya Pradesh India Department of Computer Science and Engineering Chennai Institute of Technology Malayambakkam Tamil Nadu India Department of Computer Science and Engineering Sri Sairam Engineering College Chennai Tamil Nadu India Department of MBA-Human Resource Management R.V. Institute of Management Bangalore Karnataka India

Since then, the use of the blockchains for emerging technologies such as AI has been explored by leading community science, where several actors or agents work together to settle for consensus. To do this, data must be converted into blockchain information in blockchain storage. This blockchains are referred to as blockchains based on information. Information blockchains can be useful for effective implementation of risk management. A previous work implemented the blockchain probabilistic which renders blockchains dependent on information simpler. This article suggests an extension of the definition of the stochastic blockchain. It is suggested that a social marketing system should be developed for such blockchains. It is planned to satisfy the needs of a wide variety of applications. In general, we use it to detect relevant nodes and reduce its effect on the consensus mechanism of the stochastic blockchains. We test the system using multiple adversary tactics by applying it to a benchmark. We also evaluate unilateral decisions both with and without identification of malicious nodes. Both outcomes indicate a sustainable efficiency, in which the planned work outcomes and results are outstanding.

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Application of bioinformatics and scalable computing to perform proteomic analysis of stomach tissue from diabetic mice

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Scalable Computing 2007年第2期8卷 173-183页

作者： List, Edward O. Berryman, Darlene E. Palmer, Amanda J. Gosney, Elahu Okada, Shigeru Kelder, Bruce Lichtenberg, Jens Welch, Lonnie R. Kopchick, John J. Edison Biotechnology Institute Ohio University United States School of Human and Consumer Sciences Ohio University United States Department of Pediatrics College of Osteopathic Medicine Ohio University United States School of Electrical Engineering and Computer Science Ohio University United States Department of Biomedical Sciences College of Osteopathic Medicine Ohio University United States Edison Biotechnology Institute Ohio University 101 Konneker Research Laboratories The Ridges AthensOH45701 United States

Modern molecular biology experiments generate large amounts of data, which the biologists then have to analyze these manually through slow and error-prone processes. Bioinformatics and scalable computing provide essential tools for a speedup of the proteomics analysis. An example for such a proteomic analysis and the influences of bioinformatics is presented in this paper. The stomach is a versatile organ with a prominent role in digestion and with endocrine function. While stomach has been the target of several proteomic analyses interested in cancer or ulcer biology, no studies have analyzed the proteome of stomach with the onset of diabetes, despite the fact that diabetes has a significant impact on gastric function. In this study, proteomic analyses were performed on stomach samples collected from C57BL/6J mice with obesity and diabetes and compared with samples from non-diabetic, lean controls. Obesity and diabetes were induced in mice by placing 3 week old mice on a high fat diet for 16 weeks, while control mice remained on a low fat standard chow diet. Once diabetes was established in the high fat fed mice, 4 diabetic and 4 control mice were sacrificed and stomachs removed for proteomic analysis using 2 dimension gel electrophoresis (2-DE). The protein "spots" that made up the stomach proteomic profiles were quantified using PDQuest 7.0.0 software. Protein spots found to be increased or decreased in diabetic stomach as compared to control stomach were removed from the gel for identification by database searches from peak lists generated by both MALDI-TOF and MS/MS analyses. In conclusion, a total of 23 proteins are reported herein with 14 being increased and 9 being decreased in diabetic stomach as compared to control. © 2007 SWPS.

关键词： Electrophoresis

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[email protected]: Theoretical and Methodological Challenges in Intelligent Conversational User Interface interactions

[email protected]: Theoretical and Methodological Challenges...

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26th International Conference on Intelligent User Interfaces - Companion

作者： Philip R Doyle Daniel John Rough Justin Edwards Benjamin R. Cowan Leigh Clark Martin Porcheron Stephan Schlögl María Inés Torres Cosmin Munteanu Christine Murad Jaisie Sin Minha Lee Matthew Peter Aylett Heloisa Candello School of Information & Communication Studies University College Dublin Ireland School of Science and Engineering University of Dundee United Kingdom Information and Communication Studies University College Dublin Ireland Computer Science Swansea University United Kingdom Dept. Management Communication & IT MCI Management Center Innsbruck Austria Speech Interactive Research Group Universidad del Pais Vasco UPV/EHU Spain Institute for Communication Culture Information and Technology University of Toronto Mississauga Canada University of Toronto Canada Human Technology Interaction Technical University of Eindhoven Netherlands CereProc Ltd. United Kingdom IBM Research Brazil

ISBN: (纸本)9781450380188

This workshop aims to bring together the Intelligent User Interface (IUI) and Conversational User Interface (CUI) communities to understand the theoretical and methodological challenges in designing, deploying and evaluating CUIs. CUIs have continued to prosper with the increased use and technological developments in both text-based chatbots and speech-based systems. However, challenges remain in creating established theoretical and methodological approaches for CUIs, and how these can be used with recent engineering advances. These include assessing the impact of interface design on user behaviours and perceptions, developing design guidelines, understanding the role of personalisation and issues of ethics and privacy. Our half-day multidisciplinary workshop brings together researchers and practitioners from the IUI and CUI communities in academia and industry. We aim to (1) identify and map out key focus areas and research challenges to address these critical theoretical and methodological gaps and (2) foster strong relationships between disciplines within and related to Artificial Intelligence (AI) and human-computer interaction (HCI).

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Correlation-Distance Graph Learning for Treatment Response Prediction from rs-fMRI

arXiv

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arXiv 2023年

作者： Zhang, Xiatan Zheng, Sisi Shum, Hubert P.H. Zhang, Haozheng Song, Nan Song, Mingkang Jia, Hongxiao Beijing Key Laboratory of Mental Disorders National Clinical Research Center for Mental Disorders National Center for Mental Disorders Beijing Anding Hospital Capital Medical University Beijing China Advanced Innovation Center for Human Brain Protection Beijing Anding Hospital Capital Medical University Beijing China Department of Computer Science Durham University Durham United Kingdom

Resting-state fMRI (rs-fMRI) functional connectivity (FC) analysis provides valuable insights into the relationships between different brain regions and their potential implications for neurological or psychiatric disorders. However, specific design efforts to predict treatment response from rs-fMRI remain limited due to difficulties in understanding the current brain state and the underlying mechanisms driving the observed patterns, which limited the clinical application of rs-fMRI. To overcome that, we propose a graph learning framework that captures comprehensive features by integrating both correlation and distance-based similarity measures under a contrastive loss. This approach results in a more expressive framework that captures brain dynamic features at different scales and enables more accurate prediction of treatment response. Our experiments on the chronic pain and depersonalization disorder datasets demonstrate that our proposed method outperforms current methods in different scenarios. To the best of our knowledge, we are the first to explore the integration of distance-based and correlation-based neural similarity into graph learning for treatment response prediction. © 2023, CC BY.

关键词： Forecasting

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Optimizing Heart Disease Prediction Models through SMOTE: Addressing Data Imbalance

Optimizing Heart Disease Prediction Models through SMOTE: Ad...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Waheeb Baddah Hamzah Ali Qasem Ayman Alsabry Rana Saleh Al Gawani Wafa Mohammed Alzuraiqi F. E. Hanash Azal University for Human Development International University of Technology Twintech Sana’a Yemen International University of Technology Twintech 21 September University of Medical and Applied Sciences Sana’a Yemen Department of Computer Science International University of Technology Twintech Sana’a Yemen Lebanese International University Sana’a Yemen Emirates International University International University of Technology Twintech Sana’a Yemen

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

The problem of data imbalance poses a significant challenge in the field of medical diagnostics, particularly in heart disease prediction using machine learning models. This study investigates the application of the Synthetic Minority Over-sampling Technique (SMOTE) to address this imbalance and improve the predictive accuracy of heart disease models. Through a rigorous methodology involving data collection, preprocessing, and the evaluation of 21 machine learning models, the study compares the performance of models trained on both original and SMOTE-balanced datasets. The findings indicate that models trained on SMOTE-balanced datasets showed statistically significant improvements in precision, F1-score, and often recall, with varied impacts on accuracy depending on the dataset. Key performance metrics such as accuracy, precision, recall, and F1-score are analyzed using t-tests to assess the statistical significance of improvements offered by SMOTE. Notably, the enhancements were particularly evident in precision and F1-score across the Cleveland and Statlog heart disease datasets, demonstrating SMOTE's ability to enhance model performance effectively. This study aims to demonstrate the potential of SMOTE in enhancing model performance, thus contributing to more effective and reliable heart disease diagnostics.

关键词： Heart Measurement Accuracy Machine learning Predictive models Data collection Data models

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Accelerating Sustainability through Leveraging Machine Learning to Analyze CSR Spending in the Indian Automobile Industry

Accelerating Sustainability through Leveraging Machine Learn...

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International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： Kottala Sri Yogi Dankan Gowda V Anwesha Pati Ved Srinivas Santosh Aghav Ibrahim Abdullah Department of Operations Symbiosis Institute of Business Management Hyderabad Symbiosis International University Pune India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka Symbiosis Institute of Business Management Hyderabad India Department of Human Resources Thiagarajar School of Management Madurai Tamil Nadu India Symbiosis Law School Hyderabad Symbiosis International (Deemed University) Pune India Department of Computer Science Al-Turath University College Baghdad Iraq

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

This paper uses machine learning to assess Corporate Social Responsibility spending trends on the Indian automobile sector between 2016 and 2021. Simply put, the aim of the research is to recognize trends and patterns in sustainability expenditures. More specifically, the study examines how businesses allocate money across the three important aspects of the sustainability report: People, Planet, and Profit Purcell, 2020. The research is based on a largely industry-based database obtained from public companies’ records. The use of advanced statistical techniques, such as machine learning and random forest modeling, enables one to examine the impact of financial allocations on sustainability achievements. Increments in CSR spending are related to enhanced company sustainability performance in terms of correlation outcomes. The paper also recognizes areas where cost cuts have the best returns, as well as shortfalls in the existing CSR approach, offering a strategy for possible development’s.

关键词： Profitability Companies Market research Automobiles Synchronization Resource management Sustainable development

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An ecologically valid examination of event-based and time-based prospective memory using immersive virtual reality: The influence of attention, memory, and executive function processes on real-world prospective memory.

TechRxiv

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TechRxiv 2021年

作者： Kourtesis, Panagiotis MacPherson, Sarah E. Human Cognitive Neuroscience Department of Psychology University of Edinburgh Edinburgh United Kingdom Department of Psychology University of Edinburgh Edinburgh United Kingdom Lab of Experimental Psychology Suor Orsola Benincasa University of Naples Naples Italy Interdepartmental Centre for Planning and Research "Scienza Nuova" Suor Orsola Benincasa University of Naples Naples Italy National Research Institute of Computer Science and Automation INRIA Rennes France Univ Rennes Rennes France gResearch Institute of Computer Science and Random Systems IRISA Rennes France French National Centre for Scientific Research CNRS Rennes France Department of Psychology University of Edinburgh 7 George Square EdinburghEH8 9JZ United Kingdom

Studies on prospective memory (PM) predominantly assess either event- or time-based PM by implementing non-ecological laboratory-based tasks. The results deriving from these paradigms have provided findings that are discrepant with ecologically valid research paradigms that converge on the complexity and cognitive demands of everyday tasks. The Virtual Reality Everyday Assessment Lab (VR-EAL), an immersive virtual reality (VR) neuropsychological battery with enhanced ecological validity, was implemented to assess everyday event- and time-based PM, as well as the influence of other cognitive functions on everyday PM functioning. The results demonstrated the importance of delayed recognition, planning, and visuospatial attention on everyday PM. Delayed recognition and planning ability were found to be central in event- and time-based PM respectively. In order of importance, delayed recognition, visuospatial attention speed, and planning ability were found to be involved in event-based PM functioning. Comparably, planning, visuospatial attention accuracy, delayed recognition, and multitasking/task-shifting ability were found to be involved in time-based PM functioning. These findings further suggest the importance of ecological validity in the study of PM, which may be achieved using immersive VR paradigms. © 2021, CC BY.

关键词： Virtual reality

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VICE: variational interpretable concept embeddings 22

VICE: variational interpretable concept embeddings

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Lukas Muttenthaler Charles Y. Zheng Patrick McClure Robert A. Vandermeulen Martin N. Hebart Francisco Pereira Machine Learning Group Technische Universität Berlin Berlin Institute for the Foundations of Learning and Data (BIFOLD) Berlin Germany Machine Learning Team FMRI Facility National Institute of Mental Health Bethesda MD Department of Computer Science Naval Postgraduate School Monterey CA Vision and Computational Cognition Group MPI for Human Cognitive and Brain Sciences Leipzig Germany

ISBN: (纸本)9781713871088

A central goal in the cognitive sciences is the development of numerical models for mental representations of object concepts. This paper introduces Variational Interpretable Concept Embeddings (VICE), an approximate Bayesian method for embedding object concepts in a vector space using data collected from humans in a triplet odd-one-out task. VICE uses variational inference to obtain sparse, non-negative representations of object concepts with uncertainty estimates for the embedding values. These estimates are used to automatically select the dimensions that best explain the data. We derive a PAC learning bound for VICE that can be used to estimate generalization performance or determine a sufficient sample size for experimental design. VICE rivals or outperforms its predecessor, SPoSE, at predicting human behavior in the triplet odd-one-out task. Furthermore, VICE's object representations are more reproducible and consistent across random initializations, highlighting the unique advantage of using VICE for deriving interpretable embeddings from human behavior.

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