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Selected methodological and practical aspects of the multi-criteria method PVM

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Procedia computer science 2019年 159卷 2267-2278页

作者： Nermend Kesra Piwowarski Mateusz Wojciech Sałabun Department of Computational Methods in Experimental Economics Faculty of Economics and Management Institute of IT in Management University of Szczecin Mickiewicza 64 Szczecin 71-101 Poland Department of Artificial Intelligence Methods and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. Żołnierska 49 71-210 Szczecin Poland

In multi-criteria decision support methods a significant participation of the decision maker is required. The obtained assessment is subjective, and the decision-making process is usually difficult to automate. Limiting interaction with the decision-maker, while maintaining the characteristics of multi-criteria analysis seems to be a desirable direction of methodological research. One of the solutions going in this direction is the original multi-criteria PVM method. The applicability of the method has been verified in the context of research on the assessment of EU countries in terms of ensuring healthy living conditions.

关键词： Multiple-Criteria Decision Analysis PVM Sustainable Development Goals

The Brain Tumor Segmentation (BraTS) Challenge 2023: Glioma Segmentation in Sub-Saharan Africa Patient Population (BraTS-Africa)

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arXiv 2023年

作者： Adewole, Maruf Rudie, Jeffrey D. Gbdamosi, Anu Toyobo, Oluyemisi Raymond, Confidence Zhang, Dong Omidiji, Olubukola Akinola, Rachel Suwaid, Mohammad Abba Emegoakor, Adaobi Ojo, Nancy Aguh, Kenneth Kalaiwo, Chinasa Babatunde, Gabriel Ogunleye, Afolabi Gbadamosi, Yewande Iorpagher, Kator Calabrese, Evan Aboian, Mariam Linguraru, Marius Albrecht, Jake Wiestler, Benedikt Kofler, Florian Janas, Anastasia LaBella, Dominic Kzerooni, Anahita Fathi Li, Hongwei Bran Iglesias, Juan Eugenio Farahani, Keyvan Eddy, James Bergquist, Timothy Chung, Verena Shinohara, Russell Takeshi Wiggins, Walter Reitman, Zachary Wang, Chunhao Liu, Xinyang Jiang, Zhifan Familiar, Ariana Van Leemput, Koen Bukas, Christina Piraud, Maire Conte, Gian-Marco Johansson, Elaine Meier, Zeke Menze, Bjoern H. Baid, Ujjwal Bakas, Spyridon Dako, Farouk Fatade, Abiodun Anazodo, Udunna C. Lagos Nigeria Department of Radiation Biology Radiotherapy and Radiodiagnosis University of Lagos Lagos Nigeria Department of Radiology University of California San Diego United States Crestview Radiology Limited Lagos Nigeria Lagos University Teaching Hospital Lagos Nigeria Lagos State University Teaching Hospital Lagos Ikeja Nigeria NSIA-Kano Diagnostic Center Kano Nigeria Nnamdi Azikiwe University Teaching Hospital Anambra State Nnewi Nigeria Federal Medical Centre Ogun State Abeokuta Nigeria Federal Medical Centre Abia State Umuahia Nigeria National Hospital Abuja FCT Nigeria Benue State University Teaching Hospital Benue State Makurdi Nigeria Duke University Medical Center Department of Radiology United States University of California San FranciscoCA United States Yale University New HavenCT United States Children's National Hospital WashingtonDC United States George Washington University WashingtonDC United States Sage Bionetworks United States Department of Neuroradiology Technical University of Munich Munich Germany Helmholtz Research Center Munich Germany Duke University Medical Center Department of Radiation Oncology United States Children’s Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States University of Pennsylvania PhiladelphiaPA United States Athinoula A Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States University of Zurich Switzerland Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark Mayo Clinic MN United States Precision FDA U.S. Food and Drug Administration Silver SpringMD United States Booz Allen Hamilton McLeanVA United States Department of Radiology Perelman School of Medicine U

Gliomas are the most common type of primary brain tumors. Although gliomas are relatively rare, they are among the deadliest types of cancer, with a survival rate of less than 2 years after diagnosis. Gliomas are challenging to diagnose, hard to treat and inherently resistant to conventional therapy. Years of extensive research to improve diagnosis and treatment of gliomas have decreased mortality rates across the Global North, while chances of survival among individuals in low- and middle-income countries (LMICs) remain unchanged and are significantly worse in Sub-Saharan Africa (SSA) populations. Long-term survival with glioma is associated with the identification of appropriate pathological features on brain MRI and confirmation by histopathology. Since 2012, the Brain Tumor Segmentation (BraTS) Challenge have evaluated state-of-the-art machine learning methods to detect, characterize, and classify gliomas. However, it is unclear if the state- of-the-art methods can be widely implemented in SSA given the extensive use of lower-quality MRI technology, which produces poor image contrast and resolution and more importantly, the propensity for late presentation of disease at advanced stages as well as the unique characteristics of gliomas in SSA (i.e., suspected higher rates of gliomatosis cerebri). Thus, the BraTS-Africa Challenge provides a unique opportunity to include brain MRI glioma cases from SSA in global efforts through the BraTS Challenge to develop and evaluate computer-aided-diagnostic (CAD) methods for the detection and characterization of glioma in resource-limited settings, where the potential for CAD tools to transform healthcare are more likely. © 2023, CC BY.

关键词： Tumors

A Note on Comparison Between the Wiener Index and the Zagreb Indices

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Communications in Mathematical Research 2018年第4期34卷 296-302页

作者： YOU ZHI-FU HUANG YU-FEI Du Xian-kun School of Computer Science Guangdong Polytechnic Normal UniversityGuangzhou510665 Department of Mathematics Teaching Guangzhou Civil Aviation CollegeGuangzhou510403 不详

In this note, we correct a wrong result in a paper of Das et al. with regard to the comparison between the Wiener index and the Zagreb indices for trees （Das K C, Jeon H, Trinajstic N. The comparison between the Wiener index and the Zagreb indices and the eccentric connectivity index for trees. Discrete Appl. Math., 2014, 171：35 41）, and give a simple way to compare the Wiener index and the Zagreb indices for trees. Moreover, the comparison between the Wiener index and the Zagreb indices for unicyclic graphs is carried out.

关键词： Wiener index Zagreb indice tree unicyclic graph

Cocreating the Visualization of Digital Mobility Outcomes: Delphi-Type Process With Patients

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JMIR Formative Research 2025年 9卷 e68782页

作者： Lumsdon, Jack Wilson, Cameron Alcock, Lisa Becker, Clemens Benvenuti, Francesco Bonci, Tecla van den Brande, Koen Brittain, Gavin Brown, Philip Buckley, Ellen Caruso, Marco Caulfield, Brian Cereatti, Andrea Delgado-Ortiz, Laura Del Din, Silvia Evers, Jordi Garcia-Aymerich, Judith Gaßner, Heiko Gur Arieh, Tova Hansen, Clint Hausdorff, Jeffrey M. Hiden, Hugo Hume, Emily Kirk, Cameron Maetzler, Walter Megaritis, Dimitrios Rochester, Lynn Scott, Kirsty Sharrack, Basil Sutton, Norman Vereijken, Beatrix Vogiatzis, Ioannis Yarnall, Alison Keogh, Alison Cantu, Alma Population Health Sciences Institute Faculty of Medical Sciences Newcastle University Newcastle Upon Tyne United Kingdom School of Clinical Medicine Department of Public Health and Primary Care University of Cambridge Cambridge United Kingdom Translational and Clinical Research Institute Faculty of Medical Sciences Newcastle University Newcastle Upon Tyne United Kingdom NIHR Newcastle Biomedical Research Centre Newcastle University The Newcastle Upon Tyne Hospitals NHS Foundation Trust Newcastle upon Tyne United Kingdom Robert Bosch Society for Medical Research Stuttgart Germany Digital Geriatrics Unit Medical Centre University of Heidelberg Heidelberg Germany Mobilise-D Patient and Public Advisory Group Newcastle Upon Tyne United Kingdom School of Mechanical Aerospace and Civil Engineering University of Sheffield Sheffield United Kingdom Insigneo Institute for Silico Medicine University of Sheffield Sheffield United Kingdom Department of Neuroscience Sheffield NIHR Translational Neuroscience BRC Sheffield Teaching Hospitals NHS Foundation Trust Sheffield United Kingdom Sheffield Institute for Translational Neuroscience University of Sheffield Sheffield United Kingdom The Newcastle Upon Tyne Hospitals NHS Foundation Trust Newcastle Upon Tyne United Kingdom Division of Clinical Medicine University of Sheffield Sheffield United Kingdom Department of Electronics and Telecommunications Politecnico di Torino Turin Italy Insights Centre Data Analytics University College Dublin Dublin Ireland School of Public Health Physiotherapy and Sports Science University of College Dublin Dublin Ireland Barcelona Institute for Global Health Barcelona Spain Department of Medicine and Life Sciences Pompeu Fabra University Barcelona Spain Center for Biomedical Research Madrid Spain McRoberts BV The Hague Netherlands Department of Molecular Neurology University Hospital Erlangen Erlangen Germany Fraunhofer Institute for Integrated Circuits IIS Erlangen Germany Departmen

Background: Recent technological advances in wearable devices offer new potential for measuring mobility in real-world contexts. Mobilise-D has validated digital mobility outcomes to provide novel outcomes and end points in clinical research of 4 different long-term health conditions (Parkinson disease, multiple sclerosis, chronic obstructive pulmonary disease, and proximal femoral fracture). These outcomes also provide unique information that is important to patients;however, there is limited literature that explores the optimal methods to achieve this, such as the best way to visualize patients’ data. Objective: This study aimed to identify meaningful outcomes for each condition and how to best visualize them from the perspective of end users. methods: Using a Delphi-type protocol with patients as subject matter experts, we gathered iterative feedback on the cocreation of visualizations through 3 rounds of questionnaires. An open-ended questionnaire was used in round 1 to understand what aspects of mobility were most influenced by their health condition. These responses were mapped onto relevant digital mobility outcomes and walking experiences and then prioritized for visualization. Using patient responses, we worked alongside researchers, clinicians, and a patient advisory group to develop visualizations that depicted a week of mobility data. During rounds 2 and 3, participants rated usefulness and ease of understanding on a 5-point Likert scale and provided unstructured feedback in comment boxes for each visualization. Visualizations were refined using the feedback from round 2 before receiving further feedback in round 3. Results: Participation varied across rounds 1 to 3 (n=48, n=79, and n=78, respectively). Round 1 identified important outcomes and contexts for each health condition, such as walking speed and stride length for people with Parkinson disease or multiple sclerosis and number of steps for people with chronic obstructive pulmonary disease or prox

关键词： cocreation data visualization digital mobility outcomes mobility wearable devices

RRLFSOR: An Efficient Self-Supervised Learning Strategy of Graph Convolutional Networks

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arXiv 2021年

作者： Sun, Feng Ajith, Kumar V. Yang, Guanci Zhu, Qikui Zhang, Yiyun Zhang, Ansi Makwana, Dhruv Experimental Teaching Center for Liberal Arts Zhejiang Normal University Zhejiang Jinhua321004 China School of AI Bangalore560002 India Key Laboratory of Advanced Manufacturing Technology of Ministry of Education Guizhou University Guizhou Guiyang550025 China Department of Medical Imaging Western University LondonW1k 6NG Canada Engineering College Zhejiang Normal University Zhejiang Jinhua321004 China School of Mechanical Engineering Guizhou University Guizhou Guiyang550025 China Computer Science College Gujarat Technological University Bangalore560002 India

Graph Convolutional Networks (GCNs) are widely used in many applications yet still need large amounts of labelled data for training. Besides, the adjacency matrix of GCNs is stable, which makes the data processing strategy cannot efficiently adjust the quantity of training data from the built graph structures. To further improve the performance and the self-learning ability of GCNs, in this paper, we propose an efficient self-supervised learning strategy of GCNs, named randomly removed links with a fixed step at one region (RRLFSOR). RRLFSOR can be regarded as a new data augmenter to improve over-smoothing. RRLFSOR is examined on two efficient and representative GCN models with three public citation network datasets – Cora, PubMed, and Citeseer. Experiments on transductive link prediction tasks show that our strategy outperforms the baseline models consistently by up to 21.34% in terms of accuracy on three benchmark datasets. © 2021, CC BY-SA.

关键词： Convolution

Deciphering seasonal depression variations and interplays between weather changes, physical activity, and depression severity in real-world settings: Learnings from RADAR-MDD longitudinal mobile health study

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arXiv 2024年

作者： Zhang, Yuezhou Folarin, Amos A. Ranjan, Yatharth Cummins, Nicholas Rashid, Zulqarnain Conde, Pauline Stewart, Callum Sun, Shaoxiong Vairavan, Srinivasan Matcham, Faith Oetzmann, Carolin Siddi, Sara Lamers, Femke Simblett, Sara Wykes, Til Mohr, David C. Haro, Josep Maria Penninx, Brenda W.J.H. Narayan, Vaibhav A. Hotopf, Matthew Dobson, Richard J.B. Pratap, Abhishek Department of Biostatistics & Health Informatics Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Institute of Health Informatics University College London London United Kingdom NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust London United Kingdom Health Data Research UK London University College London London United Kingdom NIHR Biomedical Research Centre University College London Hospitals NHS Foundation Trust London United Kingdom Department of Computer Science University of Sheffield Sheffield United Kingdom Janssen Research and Development LLC TitusvilleNJ United States Department of Psychological Medicine Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom School of Psychology University of Sussex Falmer United Kingdom Teaching Research and Innovation Unit Parc Sanitari Sant Joan de Déu Fundació Sant Joan de Déu Barcelona Spain Centro de Investigación Biomédica en Red de Salud Mental Madrid Spain Department of Psychiatry Amsterdam UMC Vrije Universiteit Amsterdam Netherlands Mental Health Program Amsterdam Public Health Research Institute Amsterdam Netherlands Department of Psychology Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom South London and Maudsley NHS Foundation Trust London United Kingdom Center for Behavioral Intervention Technologies Department of Preventive Medicine Northwestern University ChicagoIL United States Boehringer Ingelheim Pharmaceuticals Inc. RidgefieldCT United States University of Washington Seattle United States

Prior research has shown that changes in seasons and weather can have a significant impact on depression severity. However, findings are inconsistent across populations, and the interplay between weather, behavior, and depression has not been fully quantified. This study analyzed real-world data from 428 participants (a subset;68.7% of the cohort) in the RADAR-MDD longitudinal mobile health study to investigate seasonal variations in depression (measured through a remote validated assessment - PHQ-8) and examine the potential interplay between dynamic weather changes, physical activity (monitored via wearables), and depression severity. The clustering of PHQ-8 scores identified four distinct seasonal variations in depression severity: one stable trend and three varying patterns where depression peaks in different seasons. Among these patterns, participants within the stable trend had the oldest average age (p=0.002) and the lowest baseline PHQ-8 score (p=0.003). Mediation analysis assessing the indirect effect of weather on physical activity and depression showed significant differences among participants with different affective responses to weather. Specifically, the temperature and day length significantly influenced depression severity, which in turn impacted physical activity levels (p Copyright © 2024, The Authors. All rights reserved.

关键词： Radar measurement

Diffusion-weighted MR spectroscopy: consensus, recommendations and resources from acquisition to modelling

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arXiv 2023年

作者： Ligneul, Clémence Najac, Chloé Döring, André Beaulieu, Christian Branzoli, Francesca Clarke, William T. Cudalbu, Cristina Genovese, Guglielmo Jbabdi, Saad Jelescu, Ileana Karampinos, Dimitrios Kreis, Roland Lundell, Henrik Marjańska, Malgorzata Möller, Harald E. Mosso, Jessie Mougel, Eloïse Posse, Stefan Ruschke, Stefan Simsek, Kadir Szczepankiewicz, Filip Tal, Assaf Tax, Chantal Oeltzschner, Georg Palombo, Marco Ronen, Itamar Valette, Julien Wellcome Centre for Integrative Neuroimaging FMRIB Nuffield Department of Clinical Neurosciences University of Oxford United Kingdom C.J. Gorter MRI Center Department of Radiology Leiden University Medical Center Netherlands School of Psychology Cardiff University Cardiff United Kingdom Departments of Biomedical Engineering and Radiology University of Alberta EdmontonAB Canada Paris Brain Institute - ICM Sorbonne University UMR S 1127 Inserm U 1127 CNRS UMR 7225 ParisF-75013 France CIBM Center for Biomedical Imaging Switzerland Animal Imaging and Technology Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland Center for Magnetic Resonance Research Department of Radiology University of Minnesota MinneapolisMN United States Department of Radiology Lausanne University Hospital Lausanne Switzerland Faculty of Biology and Medicine University of Lausanne Lausanne Switzerland Department of Diagnostic and Interventional Radiology Technical University of Munich Munich Germany MR Methodology Department for Diagnostic and Interventional Neuroradiology University of Bern Switzerland Swiss Institute for Translational and Entrepreneurial Medicine Bern Switzerland Danish Research Centre for Magnetic Resonance Centre for Functional and Diagnostic Imaging and Research Copenhagen University Hospital - Amager anf Hvidovre Denmark Department for Health Technology Technical University of Denmark Lyngby Denmark NMR Methods & Development Group Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany LIFMET EPFL Lausanne Switzerland Université Paris-Saclay CEA CNRS MIRCen Laboratoires des Maladies Neurodégénératives Fontenay-aux-Roses France Department of Neurology University of New Mexico School of Medicine AlbuquerqueNM87131 United States Department of Physics and Astronomy University of New Mexico School of Medicine AlbuquerqueNM87131 United States School of Computer Science and Informatics Cardiff University Cardiff United Kin

Brain cell structure and function reflect neurodevelopment, plasticity and ageing, and changes can help flag pathological processes such as neurodegeneration and neuroinflammation. Accurate and quantitative methods to non-invasively disentangle cellular structural features are needed and are a substantial focus of brain research. Diffusion-weighted MR spectroscopy (dMRS) gives access to diffusion properties of endogenous intracellular brain metabolites that are preferentially located inside specific brain cell populations. Despite its great potential, dMRS remains a challenging technique on all levels: from the data acquisition to the analysis, quantification, modelling and interpretation of results. These challenges were the motivation behind the organisation of the Lorentz Workshop on "Best Practices & Tools for Diffusion MR Spectroscopy" held in Leiden in September 2021. During the workshop, the dMRS community established a set of recommendations to execute robust dMRS studies. This paper provides a description of the steps needed for acquiring, processing, fitting and modelling dMRS data and provides links to useful resources. © 2023, CC BY-NC-ND.

关键词： Diffusion

Learning graphons via structured gromov-wasserstein barycenters

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arXiv 2020年

作者： Xu, Hongteng Luo, Dixin Carin, Lawrence Zha, Hongyuan Gaoling School of Artificial Intelligence Renmin University of China 59 Zhongguancun St Haidian District Beijing100872 China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China School of Computer Science and Technology Beijing Institue of Technology 5 Zhongguancun St Haidian District100811 China Department of ECE Duke University DurhamNC27708 United States School of Data Science Shenzhen Research Institute of Big Data Chinese University of Hong Kong Hong Kong

We propose a novel and principled method to learn a nonparametric graph model called graphon, which is defined in an infinite-dimensional space and represents arbitrary-size graphs. Based on the weak regularity lemma from the theory of graphons, we leverage a step function to approximate a graphon. We show that the cut distance of graphons can be relaxed to the Gromov-Wasserstein distance of their step functions. Accordingly, given a set of graphs generated by an underlying graphon, we learn the corresponding step function as the Gromov-Wasserstein barycenter of the given graphs. Furthermore, we develop several enhancements and extensions of the basic algorithm, e.g., the smoothed Gromov-Wasserstein barycenter for guaranteeing the continuity of the learned graphons and the mixed Gromov-Wasserstein barycenters for learning multiple structured graphons. The proposed approach overcomes drawbacks of prior state-of-the-art methods, and outperforms them on both synthetic and real-world data. The code is available at https://***/HongtengXu/SGWB-Graphon. © 2020, CC BY.

关键词： Graph theory

Optimal drug regimen and combined drug therapy and its efficacy in the treatment of COVID-19: An within-host modeling study

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arXiv 2020年

作者： Chhetri, Bishal Bhagat, Vijay M. Vamsi, D.K.K. Ananth, V.S. Prakash, Bhanu Muthusamy, Swapna Deshmukh, Pradeep Sanjeevi, Carani B. Department of Mathematics and Computer Science Sri Sathya Sai Institute of Higher Learning Prasanthi Nilayam Puttaparthi Anantapur DistrictAndhra Pradesh515134 India Central Leprosy Teaching and Research Institute - CLTRI Chennai India Department of Community Medicine All India Institute of Medical Sciences - AIIMS Nagpur India Vice-Chancellor Sri Sathya Sai Institute of Higher Learning - SSSIHL India Department of Medicine Karolinska Institute Stockholm Sweden

The COVID-19 pandemic has resulted in more than 30.35 million infections and 9, 50, 625 deaths in 212 countries over the last few months. Different drug intervention acting at multiple stages of pathogenesis of COVID-19 can substantially reduce the infection induced mortality. The current within-host mathematical modeling studies deals with the optimal drug regimen and the efficacy of combined therapy in treatment of COVID-19. The drugs/interventions considered include Arbidol, Remdesivir, Inteferon (INF) and Lopinavir/Ritonavir. It is concluded that these drug interventions when administered individually or in combination reduce the infected cells and viral load. Four scenarios involving administration of single drug intervention, two drug interventions, three drug interventions and all the four have been discussed. In all these scenarios the optimal drug regimen is proposed based on two methods. In the first method these medical interventions are modeled as control interventions and a corresponding objective function and optimal control problem is formulated. In this setting the optimal drug regimen is proposed. Later using the the comparative effectiveness method the optimal drug regimen is proposed based on basic reproduction number and viral load. The average infected cell count and viral load decreased the most when all the four interventions were applied together. On the other hand the average susceptible cell count decreased the best when Arbidol alone was administered. The basic reproduction number and viral count decreased the best when all the four interventions were applied together reinstating the fact obtained earlier in the optimal control setting. These findings may help physicians with decision making in treatment of life-threatening COVID-19 *** Codes 37N25, 37N35, 49K15, 92-10, 92C60 Copyright © 2020, The Authors. All rights reserved.

关键词： Optimal control systems