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npj Mental Health Research 2024年第1期3卷 1-14页

作者： Sadeghi, Misha Richer, Robert Egger, Bernhard Schindler-Gmelch, Lena Rupp, Lydia Helene Rahimi, Farnaz Berking, Matthias Eskofier, Bjoern M. Machine Learning and Data Analytics Lab (MaD Lab) Department Artificial Intelligence in Biomedical Engineering (AIBE) Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen 91052 Germany Chair of Visual Computing (LGDV) Department of Computer Science Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen 91058 Germany Chair of Clinical Psychology and Psychotherapy (KliPs) Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen 91052 Germany Translational Digital Health Group Institute of AI for Health Helmholtz Zentrum München - German Research Center for Environmental Health Neuherberg 85764 Germany

Detecting depression is a critical component of mental health diagnosis, and accurate assessment is essential for effective treatment. This study introduces a novel, fully automated approach to predicting depression severity using the E-DAIC dataset. We employ Large Language Models (LLMs) to extract depression-related indicators from interview transcripts, utilizing the Patient Health Questionnaire-8 (PHQ-8) score to train the prediction model. Additionally, facial data extracted from video frames is integrated with textual data to create a multimodal model for depression severity prediction. We evaluate three approaches: text-based features, facial features, and a combination of both. Our findings show the best results are achieved by enhancing text data with speech quality assessment, with a mean absolute error of 2.85 and root mean square error of 4.02. This study underscores the potential of automated depression detection, showing text-only models as robust and effective while paving the way for multimodal analysis. © The Author(s) 2024.

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Pooling Approach for Task Allocation in the Blockchain Based Decentralized Storage Network

Pooling Approach for Task Allocation in the Blockchain Based...

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International Conference on Network and Service Management

作者： Iman Vakilinia Shahin Vakilinia Shahriar Badsha Engin Arslan Shamik Sengupta School of Computing University of North Florida Jacksonville FL USA yPwC Data Analytics Team Montreal QC CA Computer Science and Engineering Department University of Nevada Reno NV USA

ISBN: (数字)9783903176249

ISBN: (纸本)9781728153964

Blockchain technology has provided a solid system to develop incentivization algorithms using the smart contract. Blockchain applies the distributed ledger to store transaction histories, and the information is stored across a network of computers instead of on a single server. This facilitates the development of a new set of applications such as distributed file storage systems where users can rent out their storage in return for a premium. The distributed file storage systems provide more privacy and security compared to the centralized storage models as there is no need to have a trusted party. New schemes have been developed for distributed file storage systems on top of the blockchain platform, however, the problem of task/service allocation in these models have not been studied before. In this paper, we study the task/service allocation in the distributed file storage systems considering the challenge of computation cost. First, we formalize the problem of task/service allocation in a decentralized storage network, and then we discuss different approaches to allocate storage tasks to storage servers in an efficient manner. Moreover, we study the benefits of the cooperation (a.k.a pooling) in the storage and retrieval markets of distributed storage networks. The evaluation results show the benefit of our proposed pooling based approach in storage and retrieval markets.

关键词： Task analysis Servers Contracts Resource management Computational modeling Internet

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Infectious disease surveillance needs for the United States: lessons from COVID-19

arXiv

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arXiv 2023年

作者： Lipsitch, Marc Bassett, Mary T. Brownstein, John S. Elliott, Paul Eyre, David Grabowski, M. Kate Hay, James A. Johansson, Michael Kissler, Stephen M. Larremore, Daniel B. Layden, Jennifer Lessler, Justin Lynfield, Ruth MacCannell, Duncan Madoff, Lawrence C. Metcalf, Jessica E. Meyers, Lauren A. Ofori, Sylvia K. Quinn, Celia Ramos Bento, Ana I. Reich, Nick Riley, Steven Rosenfeld, Roni Samore, Matthew H. Sampath, Rangarajan Slayton, Rachel B. Swerdlow, David L. Truelove, Shaun Varma, Jay K. Grad, Yonatan H. Center for Forecasting and Outbreak Analytics US Centers for Disease Control and Prevention AtlantaGA United States Center for Communicable Disease Dynamics Department of Epidemiology Harvard T.H. Chan School of Public Health BostonMA United States François-Xavier Bagnoud Center for Health and Human Rights Department of Social and Behavioral Sciences The Harvard T.H. Chan School of Public Health BostonMA United States Boston Children’s Hospital Harvard Medical School BostonMA United States Department of Epidemiology and Public Health Medicine Imperial College London London United Kingdom Big Data Institute Nuffield Department of Population Health University of Oxford Oxford United Kingdom Department of Epidemiology Bloomberg School of Public Health Johns Hopkins University BaltimoreMD United States Division of Vector-Borne Diseases US Centers for Disease Control and Prevention San JuanPR United States Department of Computer Science University of Colorado Boulder BoulderCO United States BioFrontiers Institute University of Colorado Boulder BoulderCO United States Office of Public Health Data Surveillance and Technology US Centers for Disease Control and Prevention AtlantaGA United States Department of Epidemiology UNC Gillings School of Public Health Chapel HillNC United States Minnesota Department of Health MinneapolisMN United States Office of Advanced Molecular Detection US Centers for Disease Control and Prevention AtlantaGA United States Massachusetts Department of Public Health BostonMA United States Department of Ecology and Evolutionary Biology Princeton University PrincetonNJ United States Department of Integrative Biology University of Texas at Austin AustinTX United States Division of Disease Control New York City Department of Health and Mental Hygiene New York CityNY United States Pandemic Prevention Initiative The Rockefeller Foundation WashingtonDC United States Department of Biostatistics University of Massachusetts Amh

The COVID-19 pandemic has highlighted the need to upgrade systems for infectious disease surveillance and forecasting and modeling of the spread of infection, both of which inform evidence-based public health guidance and policies. Here, we discuss requirements for an effective surveillance system to support decision making during a pandemic, drawing on the lessons of COVID-19 in the U.S., while looking to jurisdictions in the U.S. and beyond to learn lessons about the value of specific data types. In this report, we define the range of decisions for which surveillance data are required, the data elements needed to inform these decisions and to calibrate inputs and outputs of transmission-dynamic models, and the types of data needed to inform decisions by state, territorial, local, and tribal health authorities. We define actions needed to ensure that such data will be available and consider the contribution of such efforts to improving health equity. © 2023, CC BY.

关键词： COVID-19

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Whales, Dolphins, or Minnows? Towards the Player Clustering in Free Online Games Based on Purchasing Behavior via data Mining Technique

Whales, Dolphins, or Minnows? Towards the Player Clustering ...

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2018 IEEE International Conference on Big data, Big data 2018

作者： Yang, Wanshan Yang, Gemeng Huang, Ting Chen, Lijun Liu, Youjian Eugene Department of Computer Science University of Colorado Boulder BoulderCO United States Department of Data Analytics Yoozoo Games Shanghai China Department of Electrical Computer and Energy Engineering University of Colorado Boulder BoulderCO United States

ISBN: (纸本)9781538650356

The free online game market is one of the fastest growing and most profitable markets in the electronic game industry. In order to improve players' gaming experience and increase revenue, game data analytics is usually applied to support game development, in-game event design, and game operation. In particular, the game companies strive to understand and characterize the players' buying power and design marketing strategies or incentives accordingly to increase their revenues. In this paper, we propose a novel algorithm to perform player clustering into the groups of high spenders (whales), moderate spenders (dolphins), and low spenders (minnows) based on the players' purchase records via data mining technique. Evaluations including an A/B test with a typical popular free online game show that our proposed algorithm can effectively cluster the players in terms of their purchasing behavior and the game companies can benefit from our algorithm. © 2018 IEEE.

关键词： data mining

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Trajectory clustering of road traffic in urban environments using incremental machine learning in combination with hyperdimensional computing

Trajectory clustering of road traffic in urban environments ...

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International Conference on Intelligent Transportation

作者： Tharindu Bandaragoda Daswin De Silva Denis Kleyko Evgeny Osipov Urban Wiklund Damminda Alahakoon Research Centre for Data Analytics and Cognition La Trobe University Melbourne Australia Department of Computer Science Electrical and Space Engineering Lulea University of Technology Lulea Sweden Umea University Umea Sweden

ISBN: (纸本)9781538670255

Road traffic congestion in urban environments poses an increasingly complex challenge of detection, profiling and prediction. Although public policy promotes transport alternatives and new infrastructure, traffic congestion is highly prevalent and continues to be the lead cause for numerous social, economic and environmental issues. Although a significant volume of research has been reported on road traffic prediction, profiling of traffic has received much less attention. In this paper we address two key problems in traffic profiling by proposing a novel unsupervised incremental learning approach for road traffic congestion detection and profiling, dynamically over time. This approach uses (a) hyperdimensional computing to enable capture variable-length trajectories of commuter trips represented as vehicular movement across intersections, and (b) transforms these into feature vectors that can be incrementally learned over time by the Incremental Knowledge Acquiring Self-Learning (IKASL) algorithm. The proposed approach was tested and evaluated on a dataset consisting of approximately 190 million vehicular movement records obtained from 1,400 Bluetooth identifiers placed at the intersections of the arterial road network in the State of Victoria, Australia.

关键词： Trajectory Roads Machine learning Transforms Heuristic algorithms Machine learning algorithms Urban areas

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Artificial intelligence reveals brain aging patterns in 27,402 individuals without diagnosed cognitive impairment that are linked to genetics, biomedical measures, and cognitive decline

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Alzheimer's & Dementia 2023年第S17期19卷

作者： Ioanna Skampardoni Ilya M. Nasrallah Junhao Wen Yuhan Cui Ahmed Abdulkadir Zhijian Yang Guray Erus Elizabeth Mamourian Ashish Singh Haochang Shou Li Shen Konstantina Nikita Christos Davatzikos iSTAGING consortium School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Centre for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA Correspondece Department of Radiology University of Pennsylvania Philadelphia PA USA Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Laboratory for Research in Neuroimaging Department of Clinical Neurosciences Lausanne University Hospital (CHUV) and University of Lausanne Lausanne Switzerland 1Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Biostatistics Epidemiology & Informatics University of Pennsylvania Philadelphia PA USA University of Pennsylvania Philadelphia PA USA

Background Understanding heterogeneity of structural brain changes in aging may provide insights into susceptibility to neurodegenerative diseases. We characterize the genetics underlying brain structural heterogeneity within cognitively unimpaired (CU) individuals using data-driven machine learning applied to a diverse dataset of 27,402 individuals from 11 neuroimaging studies from the iSTAGING consortium. Method Structural brain morphologic patterns of CU individuals were independently examined in four decade-long intervals spanning ages 45 to 85. Within each interval, Smile-GAN (Yang et al., 2021) was trained on baseline anatomic and white matter hyperintensity (WMH) volumes. Smile-GAN probability scores were used as phenotypes in genome-wide association studies (GWAS). Specifically, we performed multiple linear regressions controlling for confounders (e.g., age) via Plink (Purcell et al., 2007). We observed longitudinal clustering stability across decades, so individuals from adjacent age groups were combined into broader age groups ([45,65), [65,85)) due to the large sample requirement of GWAS. Genomic loci, represented by the top leading single nucleotide polymorphisms (SNPs), were defined considering linkage disequilibrium. We investigated associations of SNPs with clinical traits and mapped them to genes using the GWAS Catalog (Buniello et al., 2019). Result Three structural brain aging patterns, relative to resilient agers (A0), consistent across decades, emerged: A1, or ‘typical’ aging with low atrophy and WMHs, and two ‘advanced’ aging patterns, one showing elevated WMHs and modest atrophy (A2) and the other displaying severe, widespread atrophy and moderate WMH load (A3) (Figure 1). GWAS discovered eight and six genomic loci in [45,65) and [65,85) age groups, respectively (Table 1, Figure 2). The lead SNPs for A1 and A2 were previously associated with several cardiometabolic risk factors, WMHs, and regional brain volumes. Interestingly, rs4843552, previo

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Preface

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2022年 13469 LNAI卷 v-vi页

作者： Bringas, Pablo García García, Hilde Pérez de Pisón, Francisco Javier Martínez Flecha, José Ramón Villar Lora, Alicia Troncoso de la Cal, Enrique A. Herrero, Álvaro Álvarez, Francisco Martínez Psaila, Giuseppe Quintián, Héctor Corchado, Emilio University of Deusto Bilbao Spain University of León León Spain University of La Rioja La Rioja Logroño Spain University of Oviedo Oviedo Spain Data Science and Big Data Analytics Lab Pablo de Olavide University Sevilla Spain Department of Computer Science University of Oviedo Oviedo Spain Applied Computational Intelligence University of Burgos Burgos Burgos Spain Universidad Pablo de Olavide Seville Spain DIGIP University of Bergamo Dalmine Bergamo Italy Department of Industrial Engineering University of A Coruña Ferrol Spain University of Salamanca Salamanca Spain

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Moving towards informative and actionable social media research

arXiv

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arXiv 2025年

作者： Bak-Coleman, Joseph B. Lewandowsky, Stephan Lorenz-Spreen, Philipp Narayanan, Arvind Orben, Amy Oswald, Lisa Centre for the Advanced Study of Collective Behavior University of Konstanz Konstanz Germany Department of Collective Behavior Max Planck Institute of Animal Behavior Konstanz Germany Department of Biology University of Konstanz Konstanz Germany Santa Fe Institute Santa FeNM United States Berkman Klein Center Harvard University CambridgeMA United States School of Psychological Science University of Bristol Bristol United Kingdom Department of Psychology University of Potsdam Potsdam Germany Center Synergy of Systems Center for Scalable Data Analytics and Artificial Intelligence Dresden University of Technology Dresden Germany Center for Adaptive Rationality Max Planck Institute for Human Development Berlin Germany Center for Information Technology Policy Princeton University PrincetonNJ United States Department of Computer Science Princeton University PrincetonNJ United States MRC Cognition and Brain Sciences Unit University of Cambridge Cambridge United Kingdom

Social media is nearly ubiquitous in modern life, and concerns have been raised about its putative societal impacts, ranging from undermining mental health and exacerbating polarization to fomenting violence and disrupting democracy. Despite extensive research, consensus on these effects remains elusive, with observational studies often highlighting concerns while randomized controlled trials (RCTs) yield conflicting or null findings. This review examines how the complexity inherent in social systems can account for such discrepancies, emphasizing that emergent societal and long-term outcomes cannot be readily inferred from individual-level effects. In complex systems, such as social networks, feedback loops, hysteresis, multi-scale dynamics, and non-linearity limit the utility of approaches for assessing causality that are otherwise robust in simpler contexts. Revisiting large-scale experiments, we explore how null or conflicting findings may reflect these complexities rather than a true absence of effects. Even in cases where the methods are appropriate, assessing the net impacts of social media provides little actionable insight given that eliminating social media is not a realistic option for whole populations. We argue that progress will require a complexity-minded approach focused on specific design choices of online platforms that triangulates experimental, observational and theoretical methods. © 2025, CC BY.

关键词： Tweets

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Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and east Asian ancestries (vol 55, pg 89, 2023)

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NATURE GENETICS 2023年第3期55卷 519-520页

作者： Fernandez-Rozadilla, Ceres Timofeeva, Maria Chen, Zhishan Law, Philip Thomas, Minta Schmit, Stephanie Diez-Obrero, Virginia Hsu, Li Fernandez-Tajes, Juan Palles, Claire Sherwood, Kitty Briggs, Sarah Svinti, Victoria Donnelly, Kevin Farrington, Susan Blackmur, James Vaughan-Shaw, Peter Shu, Xiao-ou Long, Jirong Cai, Qiuyin Guo, Xingyi Lu, Yingchang Broderick, Peter Studd, James Huyghe, Jeroen Harrison, Tabitha Conti, David Dampier, Christopher Devall, Mathew Schumacher, Fredrick Melas, Marilena Rennert, Gad Obon-Santacana, Mireia Martin-Sanchez, Vicente Moratalla-Navarro, Ferran Oh, Jae Hwan Kim, Jeongseon Jee, Sun Ha Jung, Keum Ji Kweon, Sun-Seog Shin, Min-Ho Shin, Aesun Ahn, Yoon-Ok Kim, Dong-Hyun Oze, Isao Wen, Wanqing Matsuo, Keitaro Matsuda, Koichi Tanikawa, Chizu Ren, Zefang Gao, Yu-Tang Jia, Wei-Hua Hopper, John Jenkins, Mark Win, Aung Ko Pai, Rish Figueiredo, Jane Haile, Robert Gallinger, Steven Woods, Michael Newcomb, Polly Duggan, David Cheadle, Jeremy Kaplan, Richard Maughan, Timothy Kerr, Rachel Kerr, David Kirac, Iva Bohm, Jan Mecklin, Lukka-Pekka Jousilahti, Pekka Knekt, Paul Aaltonen, Lauri Rissanen, Harri Pukkala, Eero Eriksson, Johan Cajuso, Tatiana Hanninen, Ulrika Kondelin, Johanna Palin, Kimmo Tanskanen, Tomas Renkonen-Sinisalo, Laura Zanke, Brent Mannisto, Satu Albanes, Demetrius Weinstein, Stephanie Ruiz-Narvaez, Edward Palmer, Julie Buchanan, Daniel Platz, Elizabeth Visvanathan, Kala Ulrich, Cornelia Siegel, Erin Brezina, Stefanie Gsur, Andrea Campbell, Peter Chang-Claude, Jenny Hoffmeister, Michael Brenner, Hermann Slattery, Martha Potter, John Tsilidis, Konstantinos Schulze, Matthias Gunter, Marc Murphy, Neil Castells, Antoni Castellvi-Bel, Sergi Moreira, Leticia Arndt, Volker Shcherbina, Anna Stern, Mariana Pardamean, Bens Bishop, Timothy Giles, Graham Southey, Melissa Idos, Gregory McDonnell, Kevin Abu-Ful, Zomoroda Greenson, Joel Shulman, Katerina Lejbkowicz, Flavio Offit, Kenneth Su, Yu-Ru Steinfelder, Robert Keku, Temitope van Guelpen, Bethany Hudson, Thomas Hampel, Heather Pearlman, Edinburgh Cancer Research Centre Institute of Genomics and Cancer University of Edinburgh Edinburgh UK Genomic Medicine Group Instituto de Investigacion Sanitaria de Santiago Santiago de Compostela Spain Colon Cancer Genetics Group Medical Research Council Human Genetics Unit Institute of Genetics and Cancer University of Edinburgh Edinburgh UK Danish Institute for Advanced Study Department of Public Health University of Southern Denmark Odense Denmark Division of Epidemiology Department of Medicine Vanderbilt-Ingram Cancer Center Vanderbilt Epidemiology Center Vanderbilt University Medical Center Nashville TN USA Division of Genetics and Epidemiology Institute of Cancer Research London UK Public Health Sciences Division Fred Hutchinson Cancer Research Center Seattle WA USA Genomic Medicine Institute Cleveland Clinic Cleveland OH USA Population and Cancer Prevention Program Case Comprehensive Cancer Center Cleveland OH USA Colorectal Cancer Group ONCOBELL Program Bellvitge Biomedical Research Institute Barcelona Spain Oncology Data Analytics Program Catalan Institute of Oncology Barcelona Spain Consortium for Biomedical Research in Epidemiology and Public Health Madrid Madrid Spain Department of Clinical Sciences Faculty of Medicine University of Barcelona Barcelona Spain Department of Biostatistics School of Public Health University of Washington Seattle WA USA Institute of Cancer and Genomic Sciences College of Medical and Dental Sciences University of Birmingham Birmingham UK Department of Public Health Richard Doll Building University of Oxford Oxford UK Department of Biomedical Informatics Vanderbilt University School of Medicine Nashville TN USA Department of Preventive Medicine USC Norris Comprehensive Cancer Center Keck School of Medicine University of Southern California Los Angeles CA USA Center for Public Health Genomics Department of Public Health Sciences University of Virginia Charlottesville VA USA Department of Populat

Colorectal cancer (CRC) is a leading cause of mortality worldwide. We conducted a genome-wide association study meta-analysis of 100,204 CRC cases and 154,587 controls of European and east Asian ancestry, identifying 205 independent risk associations, of which 50 were unreported. We performed integrative genomic, transcriptomic and methylomic analyses across large bowel mucosa and other tissues. Transcriptome- and methylome-wide association studies revealed an additional 53 risk associations. We identified 155 high-confidence effector genes functionally linked to CRC risk, many of which had no previously established role in CRC. These have multiple different functions and specifically indicate that variation in normal colorectal homeostasis, proliferation, cell adhesion, migration, immunity and microbial interactions determines CRC risk. Crosstissue analyses indicated that over a third of effector genes most probably act outside the colonic mucosa. Our findings provide insights into colorectal oncogenesis and highlight potential targets across tissues for new CRC treatment and chemoprevention strategies.

关键词： Colorectal cancer Epigenomics Genome-wide association studies Transcriptomics

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Innovative informatics methods for process mining in health care

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Journal of Biomedical Informatics 2020年 109卷 103551页

作者： Munoz-Gama, Jorge Martin, Niels Fernandez-Llatas, Carlos Johnson, Owen Sepúlveda, Marcos Department of Computer Science School of Engineering Pontificia Universidad Católica de Chile Santiago Chile Research Group Business Informatics Hasselt University Hasselt Belgium Data Analytics Laboratory Vrije Universiteit Brussel Brussels Belgium ITACA Institute – Process Mining 4 Health Lab Universitat Politècnica de Valencia Valencia Spain Karolinska Institutet Stockholm Sweden School of Computing Leeds University Leeds United Kingdom

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