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Data mining-based model and risk prediction of colorectal cancer by using secondary health data:A systematic review

Chinese Journal of Cancer Research 2020年第2期32卷 242-251页

作者： Hailun Liang Lei Yang Lei Tao Leiyu Shi Wuyang Yang Jiawei Bai Da Zheng Ning Wang Jiafu Ji School of Public Administration and Policy Renmin University of ChinaBeijing 100872China Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education/Beijing) Beijing Office for Cancer Prevention and ControlPeking University Cancer Hospital&InstituteBeijing 100142China Departm ent of Public Policy City University of Hong KongHong Kong SAR999077China Johns Hopkins Primary Care Policy Center BaltimoreMD 21205USA Ddepartment of Neurosurgery Johns Hopkins MedicineBaltimoreMD 21205USA Department of Biostatistics Johns Hopkins Bloomberg School of Public HealthBaltimoreMD 21205USA Department of Computer Science Johns Hopkins Whiting School of EngineeringBaltimoreMD 21205USA Key Laboratory of Carcinogenesis and Translational Research(Ministiy of Education/Beijing) Center of Gastrointestinal SurgeryPeking University Cancer Hospital&InstituteBeijing 100142China

Objective:Prevention and early detection of colorectal cancer(CRC)can increase the chances of successful treatment and reduce *** data mining technologies have been utilized to strengthen the early detection of CRC in primary *** synthesis on the model’s effectiveness is *** systematic review synthesizes studies that examine the effect of data mining on improving risk prediction of ***:The PRISMA framework guided the conduct of this *** obtained papers via Pub Med,Cochrane Library,EMBASE and Google *** appraisal was performed using Downs and Black’s quality *** evaluate the performance of included models,the values of specificity and sensitivity were comparted,the values of area under the curve(AUC)were plotted,and the median of overall AUC of included studies was ***:A total of 316 studies were reviewed for full *** articles were *** studies implement techniques including artificial neural networks,Bayesian networks and decision *** articles reported the overall model ***,the median AUC is 0.8243[interquartile range(IQR):0.8050-0.8886].In the two articles that reported comparison results with traditional models,the data mining method performed better than the traditional models,with the best AUC improvement of 10.7%.Conclusions:The adoption of data mining technologies for CRC detection is at an early *** numbers of included articles and heterogeneity of those studies implied that more rigorous research is expected to further investigate the techniques’effects.

关键词： Systematic review colorectal cancer disease detection data mining

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GSLB: the graph structure learning benchmark 23

GSLB: the graph structure learning benchmark

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Zhixun Li Liang Wang Xin Sun Yifan Luo Yanqiao Zhu Dingshuo Chen Yingtao Luo Xiangxin Zhou Qiang Liu Shu Wu Jeffrey Xu Yu Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences and School of Artificial Intelligence University of Chinese Academy of Sciences and Department of Automation University of Science and Technology of China Department of Automation University of Science and Technology of China School of Cyberspace Security Beijing University of Posts and Telecommunications Department of Computer Science University of California Los Angeles Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences and School of Artificial Intelligence University of Chinese Academy of Sciences Heinz College of Information Systems and Public Policy Machine Learning Department School of Computer Science Carnegie Mellon University

Graph Structure Learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the computation graph structure simultaneously. Despite the proliferation of GSL methods developed in recent years, there is no standard experimental setting or fair comparison for performance evaluation, which creates a great obstacle to understanding the progress in this field. To fill this gap, we systematically analyze the performance of GSL in different scenarios and develop a comprehensive Graph Structure Learning Benchmark (GSLB) curated from 20 diverse graph datasets and 16 distinct GSL algorithms. Specifically, GSLB systematically investigates the characteristics of GSL in terms of three dimensions: effectiveness, robustness, and complexity. We comprehensively evaluate state-of-the-art GSL algorithms in node- and graph-level tasks, and analyze their performance in robust learning and model complexity. Further, to facilitate reproducible research, we have developed an easy-to-use library for training, evaluating, and visualizing different GSL methods. Empirical results of our extensive experiments demonstrate the ability of GSL and reveal its potential benefits on various downstream tasks, offering insights and opportunities for future research. The code of GSLB is available at: https://***/GSL-Benchmark/GSLB.

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Meaningful human control: actionable properties for AI system development

arXiv

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arXiv 2021年

作者： Siebert, Luciano Cavalcante Lupetti, Maria Luce Aizenberg, Evgeni Beckers, Niek Zgonnikov, Arkady Veluwenkamp, Herman Abbink, David Giaccardi, Elisa Houben, Geert-Jan Jonker, Catholijn M. van den Hoven, Jeroen Forster, Deborah Lagendijk, Reginald L. AiTech Interdisciplinary Research Program on Meaningful Human Control Delft University of Technology Delft Netherlands Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Faculty of Industrial Design Engineering Delft University of Technology Delft Netherlands Faculty of Mechanical Maritime and Materials Engineering Delft University of Technology Delft Netherlands Faculty of Technology Policy and Management Delft University of Technology Delft Netherlands

How can humans remain in control of artificial intelligence (AI)-based systems designed to perform tasks autonomously? Such systems are increasingly ubiquitous, creating benefits - but also undesirable situations where moral responsibility for their actions cannot be properly attributed to any particular person or group. The concept of meaningful human control has been proposed to address responsibility gaps and mitigate them by establishing conditions that enable a proper attribution of responsibility for humans;however, clear requirements for researchers, designers, and engineers are yet inexistent, making the development of AI-based systems that remain under meaningful human control challenging. In this paper, we address the gap between philosophical theory and engineering practice by identifying, through an iterative process of abductive thinking, four actionable properties for AI-based systems under meaningful human control, which we discuss making use of two applications scenarios: automated vehicles and AI-based hiring. First, a system in which humans and AI algorithms interact should have an explicitly defined domain of morally loaded situations within which the system ought to operate. Second, humans and AI agents within the system should have appropriate and mutually compatible representations. Third, responsibility attributed to a human should be commensurate with that human’s ability and authority to control the system. Fourth, there should be explicit links between the actions of the AI agents and actions of humans who are aware of their moral responsibility. We argue that these four properties will support practically-minded professionals to take concrete steps toward designing and engineering for AI systems that facilitate meaningful human control. Copyright © 2021, The Authors. All rights reserved.

关键词： Artificial intelligence

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Spatio-temporal dynamics of malaria in Rwanda between 2012 and 2022: a demography-specific analysis

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Infectious Diseases of Poverty 2024年第5期13卷 100-100页

作者： Felix K.Rubuga Paula Moraga Ayman Ahmed Emmanuel Siddig Eric Remera Giovenale Moirano Guéladio Cissé Jürg Utzinger Swiss Tropical and Public Health Institute Allschwil Switzerland University of Basel Basel Switzerland College of Medicine and Health Sciences University of Rwanda Kigali Rwanda Center for Impact Innovation and Capacity building for Health Information systems and Nutrition (CIIC-HIN) Kigali Rwanda Institute of Endemic Diseases University of Khartoum Khartoum Sudan Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Medical Microbiology and Infectious Diseases Erasmus Medical Center University Medical Center Rotterdam Rotterdam The Netherlands Faculty of Medical Laboratory Sciences University of Khartoum Khartoum Sudan Rwanda Biomedical Center Kigali Rwanda Department of Medical Science University of Turin Torino Italy Barcelona Supercomputing Center Barcelona Spain

Background Despite global efforts to reduce and eventually interrupt malaria transmission,the disease remains a pressing public health problem,especially in sub-Saharan *** study presents a detailed spatio-temporal analysis of malaria transmission in Rwanda from 2012 to *** main objective was to gain insights into the evolving patterns of malaria and to inform and tailor effective public health *** The study used yearly aggregated data of malaria cases from the Rwanda health management information *** employed a multifaceted analytical approach,including descriptive statistics and spatio-temporal analysis across three demographic groups:children under the age of 5 years,and males and females above 5 *** spatially explicit models and spatio scan statistics were utilised to examine geographic and temporal patterns of relative risks and to identify clusters of malaria *** We observed a significant increase in malaria cases from 2014 to 2018,peaking in 2016 for males and females aged above 5 years with counts of 98,645 and 116,627,respectively and in 2018 for under 5-year-old children with 84,440 cases with notable geographic *** like Kamonyi(Southern Province),Ngoma,Kayonza and Bugesera(Eastern Province)exhibited high burdens,possibly influenced by factors such as climate,vector control practices,and cross-border *** spatially explicit modeling revealed elevated relative risks in numerous districts,underscoring the heterogeneity of malaria transmission in these districts,and thus contributing to an overall rising trend in malaria cases until 2018,followed by a subsequent *** findings emphasize that the heterogeneity of malaria transmission is potentially driven by ecologic,socioeconomic,and behavioural *** The study underscores the complexity of malaria transmission in Rwanda and calls for climate adaptive,gender-,age-and district-specific strategies in

关键词： Malaria transmission Epidemiology public health Rwanda Spatio-temporal analysis Rwanda

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Cross-sector energy system resilience and interdependence in a changing climate

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Joule 2025年

作者： Xu, Luo Lin, Ning Perera, A.T.D. Poor, H. Vincent Guo, Qinglai Sun, Hongbin Oppenheimer, Michael Department of Civil and Environmental Engineering Princeton University PrincetonNJ United States Center for Policy Research on Energy and the Environment Princeton University PrincetonNJ United States Andlinger Center for Energy and the Environment Princeton University PrincetonNJ United States Department of Electrical and Computer Engineering Princeton University PrincetonNJ United States Department of Electrical Engineering Tsinghua University Beijing China School of Public and International Affairs Princeton University PrincetonNJ United States Department of Geosciences Princeton University PrincetonNJ United States High Meadows Environmental Institute Princeton University PrincetonNJ United States

Rapid global electrification is deepening cross-sector interdependence, fundamentally reshaping the resilience of energy systems in the face of intensifying climate extremes. While increased integration across energy generation, transmission, and consumption sectors can significantly enhance operational flexibility, it can also amplify the risk of cross-sector cascading failures under extreme weather events, giving rise to an emerging resilience paradox that remains insufficiently understood. This study examines evolving cross-sector interactions and their implications for climate resilience by analyzing global electrification trends and regional cases in Texas, integrated with global and downscaled projections of climate extremes. By identifying critical vulnerabilities and flexibility associated with increasing sectoral interdependence, this study highlights the necessity of adopting resilience-oriented, system-level strategies for system operators and policymakers to mitigate cross-sector cascading risks and maximize the benefits of electrification in a changing climate. © 2025 Elsevier Inc.

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Bandit-PAM: Almost linear time k-medoids clustering via multi-armed bandits

arXiv

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arXiv 2020年

作者： Tiwari, Mo Mayclin, James Piech, Chris Zhang, Martin Jinye Thrun, Sebastian Shomorony, Ilan Department of Computer Science Stanford University T.H. Chan School of Public Health Harvard University Electrical and Computer Engineering University of Illinois at Urbana-Champaign

Clustering is a ubiquitous task in data science. Compared to the commonly used k-means clustering algorithm, k-medoids clustering algorithms require the cluster centers to be actual data points and support arbitrary distance metrics, allowing for greater interpretability and the clustering of structured objects. Current state-of-the-art k-medoids clustering algorithms, such as Partitioning Around Medoids (PAM), are iterative and are quadratic in the dataset size n for each iteration, being prohibitively expensive for large datasets. We propose Bandit-PAM, a randomized algorithm inspired by techniques from multi-armed bandits, that significantly improves the computational efficiency of PAM. We theoretically prove that BanditPAM reduces the complexity of each PAM iteration from O(n2) to O(n log n) and returns the same results with high probability, under assumptions on the data that often hold in practice. We empirically validate our results on several large-scale real-world datasets, including a coding exercise submissions dataset from ***, the 10x Genomics 68k PBMC single-cell RNA sequencing dataset, and the MNIST handwritten digits dataset. We observe that Bandit-PAM returns the same results as PAM while performing up to 200x fewer distance computations. The improvements demonstrated by Bandit-PAM enable k-medoids clustering on a wide range of applications, including identifying cell types in large-scale single-cell data and providing scalable feedback for students learning computer science online. We also release Python1 and C++2 implementations of our algorithm. Copyright © 2020, The Authors. All rights reserved.

关键词： Large dataset

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Welcome from the IEEE PES ISGT Europe 2020 General Chairs

IEEE PES Innovative Smart Grid Technologies Conference Europ...

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IEEE PES Innovative Smart Grid Technologies Conference Europe 2020年 2020-October卷

作者： Lukszo, Zofia Palensky, Peter Faculty of Technology Policy and Management Delft University of Technology Netherlands Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Netherlands

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Data discovery using lossless compression-based sparse representation

arXiv

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arXiv 2021年

作者： Sabeti, Elyas Song, Peter X.K. Hero, Alfred O. Michigan Institute for Data Science University of Michigan Ann Arbor United States Department of Biostatistics School of Public Health University of Michigan Ann Arbor United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor United States

Sparse representation has been widely used in data compression, signal and image denoising, dimensionality reduction and computer vision. While overcomplete dictionaries are required for sparse representation of multidimensional data, orthogonal bases represent one-dimensional data well. In this paper, we propose a data-driven sparse representation using orthonormal bases under the lossless compression constraint. We show that imposing such constraint under the Minimum Description Length (MDL) principle leads to a unique and optimal sparse representation for one-dimensional data, which results in discriminative features useful for data discovery. © 2021, CC BY.

关键词： Image denoising

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Network externalities in cybersecurity information sharing ecosystems 1

引用

15th International Conference on the Economics of Grids, Clouds, Systems, and Services, GECON 2018

作者： Rashid, Zahid Noor, Umara Altmann, Jörn Technology Management Economics and Policy Program College of Engineering Seoul National University Seoul Korea Republic of School of Electrical Engineering and Computer Sciences National University of Sciences and Technology Islamabad Pakistan

ISBN: (数字)9783030133429

ISBN: (纸本)9783030133412

The utilization of cybersecurity information for improving security posture of an organization resulted in the evolution of cybersecurity information sharing ecosystems. In this study, we consider three stakeholders i.e. cybersecurity solution providers, information providers, and end users, who have different values. Their values depend on interrelationship among them and are also based on several value parameters. We identified six value parameters and analyzed their impacts on the values of stakeholders. A simulation model has been developed using system dynamics to analyze the impact of value parameters on the values of stakeholders. The results show that end users are the main source of value in cybersecurity information sharing ecosystems, implying an effect of demand side economies of scale. The cybersecurity solution and information providers are majorly benefiting from a growing number of end users. The value of end users is mainly affected by quality of services, quality of information and the size of trusted communities. © 2019, Springer Nature Switzerland AG.

关键词： Ecosystems

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Evaluating Offshore Electricity Market Design Considering Endogenous Infrastructure Investments: Zonal or Nodal?

arXiv

引用

arXiv 2024年

作者： Kenis, Michiel Dvorkin, Vladimir Schittekatte, Tim Bruninx, Kenneth Delarue, Erik Botterud, Audun Leuven3001 Belgium The EnergyVille Thor Park 8310 Genk3600 Belgium Boeretang 200 MolB-2400 Belgium The Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States The MIT Sloan School of Management Massachusetts Institute of Technology CambridgeMA02142 United States The Florence School of Regulation European University Institute FI Firenze50133 Italy The Faculty of Technology Policy and Management TU Delft Delft2628 Netherlands The Laboratory for Information and Decision Systems Massachusetts Institute of Technology CambridgeMA02142 United States

policy makers are formulating offshore energy infrastructure plans, including wind turbines, electrolyzers, and HVDC transmission lines. An effective market design is crucial to guide cost-efficient investments and dispatch decisions. This paper jointly studies the impact of offshore market design choices on the investment in offshore electrolyzers and HVDC transmission capacity. We present a bilevel model that incorporates investments in offshore energy infrastructure, day-ahead market dispatch, and potential redispatch actions near real-time to ensure transmission constraints are respected. Our findings demonstrate that full nodal pricing, i.e., nodal pricing both onshore and offshore, outperforms the onshore zonal combined with offshore nodal pricing or offshore zonal layouts. While combining onshore zonal with offshore nodal pricing can be considered as a second-best option, it generally diminishes the profitability of offshore wind farms. However, if investment costs of offshore electrolyzers are relatively low, they can serve as catalysts to increase the revenues of the offshore wind farms. This study contributes to the understanding of market designs for highly interconnected offshore power systems, offering insights into the impact of congestion pricing methodologies on investment decisions. Besides, it is useful towards understanding the interaction of offshore loads like electrolyzers with financial support mechanisms for offshore wind farms. © 2024, CC BY.

关键词： Offshore wind farms

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