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arXiv 2024年

作者： Idnay, Betina Xu, Zihan Adams, William G. Adibuzzaman, Mohammad Anderson, Nicholas R. Bahroos, Neil Bell, Douglas S. Bumgardner, Cody Campion, Thomas Castro, Mario Cimino, James J. Cohen, I. Glenn Dorr, David Elkin, Peter L. Fan, Jungwei W. Ferris, Todd Foran, David J. Hanauer, David Hogarth, Mike Huang, Kun Kalpathy-Cramer, Jayashree Kandpal, Manoj Karnik, Niranjan S. Katoch, Avnish Lai, Albert M. Lambert, Christophe G. Li, Lang Lindsell, Christopher Liu, Jinze Lu, Zhiyong Luo, Yuan McGarvey, Peter Mendonca, Eneida A. Mirhaji, Parsa Murphy, Shawn Osborne, John D. Paschalidis, Ioannis C. Harris, Paul A. Prior, Fred Shaheen, Nicholas J. Shara, Nawar Sim, Ida Tachinardi, Umberto Waitman, Lemuel R. Wright, Rosalind J. Zai, Adrian H. Zheng, Kai Lee, Sandra Soo-Jin Malin, Bradley A. Natarajan, Karthik Price, W. Nicholson Zhang, Rui Zhang, Yiye Xu, Hua Bian, Jiang Weng, Chunhua Peng, Yifan Department of Biomedical Informatics Columbia University Irving Medical Center New YorkNY United States Department of Population Health Sciences Weill Cornell Medicine New YorkNY United States Department of Pediatrics Boston Medical Center BostonMA United States Chobanian & Avedisian School of Medicine Boston University BostonMA United States Oregon Clinical and Translational Research Institute Oregon Health and Science University PortlandOR United States Department of Public Health Sciences University of California Davis DavisCA United States Keck School of Medicine University of Southern California Los AngelesCA United States Department of Medicine David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Department of Pathology and Laboratory Medicine University of Kentucky College of Medicine LexingtonKY United States Division of Pulmonary Critical Care and Sleep Medicine University of Kansas School of Medicine Kansas CityKS United States Department of Biomedical Informatics and Data Science Heersink School of Medicine University of Alabama BirminghamAL United States Harvard Law School Petrie-Flom Center for Health Law Policy Biotechnology and Bioethics Harvard University CambridgeMA United States Department of Biomedical Informatics University at Buffalo BuffaloNY United States Center for Clinical and Translational Science Mayo Clinic RochesterMN United States Technology and Digital Solutions Stanford Medicine Stanford University StanfordCA United States Center for Biomedical Informatics Rutgers Cancer Institute New BrunswickNJ United States Department of Learning Health Sciences University of Michigan Medical School Ann ArborMI United States University of California San Diego La Jolla CA United States Department of Biostatistics and Health Data Science School of Medicine Indiana University IndianapolisIN United States Division of Artificial Medical Intelligence in Ophthalmology Univ

This study reports a comprehensive environmental scan of the generative AI (GenAI) infrastructure in the national network for clinical and translational science across 36 institutions supported by the Clinical and Translational science Award (CTSA) Program led by the National center for Advancing Translational sciences (NCATS) of the National Institutes of Health (NIH) at the United States. With the rapid advancement of GenAI technologies, including large language models (LLMs), healthcare institutions face unprecedented opportunities and challenges. This research explores the current status of GenAI integration, focusing on stakeholder roles, governance structures, and ethical considerations by administering a survey among leaders of health institutions (i.e., representing academic medical centers and health systems) to assess the institutional readiness and approach towards GenAI adoption. Key findings indicate a diverse range of institutional strategies, with most organizations in the experimental phase of GenAI deployment. The study highlights significant variations in governance models, with a strong preference for centralized decision-making but notable gaps in workforce training and ethical oversight. Moreover, the results underscore the need for a more coordinated approach to GenAI governance, emphasizing collaboration among senior leaders, clinicians, information technology staff, and researchers. Our analysis also reveals concerns regarding GenAI bias, data security, and stakeholder trust, which must be addressed to ensure the ethical and effective implementation of GenAI technologies. This study offers valuable insights into the challenges and opportunities of GenAI integration in healthcare, providing a roadmap for institutions aiming to leverage GenAI for improved quality of care and operational efficiency. © 2024, CC BY.

关键词： Electronic health record

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Tracking the consumption junction: Temporal dependencies between articles and advertisements in dutch newspapers

arXiv

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arXiv 2019年

作者： Wevers, Melvin Gao, Jianbo Nielbo, Kristoffer L. Digital Humanities Lab KNAW Humanities Cluster Netherlands Institute of Complexity Science and Big Data Technology Guangxi University China Center for Humanities Computing Aarhus Aarhus University Denmark

Historians have regularly debated whether advertisements can be used as a viable source to study the past. Their main concern centered on the question of agency. Were advertisements a reflection of historical events and societal debates, or were ad makers instrumental in shaping society and the ways people interacted with consumer goods? Using techniques from econometrics (Granger causality test) and complexity science (Adaptive Fractal Analysis), this paper analyzes to what extent advertisements shaped or reflected society. We found evidence that indicate a fundamental difference between the dynamic behavior of word use in articles and advertisements published in a century of Dutch newspapers. Articles exhibit persistent trends that are likely to be reflective of communicative memory. Contrary to this, advertisements have a more irregular behavior characterized by short bursts and fast decay, which, in part, mirrors the dynamic through which advertisers introduced terms into public discourse. On the issue of whether advertisements shaped or reflected society, we found particular product types that seemed to be collectively driven by a causality going from advertisements to articles. Generally, we found support for a complex interaction pattern dubbed the consumption junction. Finally, we discovered noteworthy patterns in terms of causality and long-range dependencies for specific product groups. All in, this study shows how methods from econometrics and complexity science can be applied to humanities data to improve our understanding of complex cultural-historical phenomena such as the role of advertising in society. Copyright © 2019, The Authors. All rights reserved.

关键词： Statistical tests

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Neural Networks Classification for Training of Five German Longsword Mastercuts - A Novel Application of Motion Capture: Analysis of Performance of Sword Fencing in the Historical European Martial Arts (HEMA) Domain

Neural Networks Classification for Training of Five German L...

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International Symposium on Computational Intelligence and Informatics

作者： Ryszard Klempous Konrad Kluwak Ito Atsushi Tomasz Górski Jan Nikodem Wojciech Bożejko Zenon Chaczko Grzegorz Borowik Jerzy Rozenblit Marek Kulbacki Wroclaw University of Science and Technology Wroclaw Poland Faculty of Economics Chuo University Tokyo Japan Polish Naval Academy Gdynia Poland School of Electrical and Data Engineering University of Technology Sydney Sydney Australia SWPS University of Social Sciences and Humanities Warsaw Poland University of Arizona Tucson USA R&D Center Polish-Japanese Academy of Information Technology Warsaw Poland DIVE IN AI Wroclaw Poland

ISBN: (纸本)9781665426855

This paper discusses an application of motion capture in longsword fencing, a discipline experiencing rising popularity since the 1990s. Historical European Martial Arts alliance focuses on re-enacting the Late Middle Ages and Renaissance fighting styles. To popularize this art, novel research to automatically distinguish selected sword cutting techniques has been conducted. The fencing knowledge required for conducting this research was based on publications and consultation with experts in the field, and recordings. For this research, different movements from Masterstrikes such as Zornhau (Strike of Wrath), Schielhau (Squinting Strike), Zwerchhau (Cross Strike), Krumphau (Crooked Strike), Scheitelhau (Crown Strike) were selected. Motions performed by an adept fencer (acting expert) were used as patterns of correct strikes and compared with the movements of fencing amateurs. The main goal of this research was to measure the precision of movement while performing five different fencing strokes. Each movement was recorded with 39 unique full-body plug-in gait configurations initially designed for medical applications. During the exercise, 16 EMG electrodes configuration was used for the measurement of muscle activity.

关键词： Training Electrodes Art Neural networks Europe Medical services Forestry

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Patients’ evaluation of information provision in the memory clinic

Alzheimer's & Dementia: Behavior & Socioeconomics of Aging

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Alzheimer's & Dementia: Behavior & Socioeconomics of Aging 2025年第1期1卷

作者： Heleen M. A. Hendriksen Jolanda H. M. Dobbe Casper de Boer Afina W. Lemstra Yolande A. L. Pijnenburg Mukrabe E. Tewolde Ellen M. A. Smets Leonie N. C. Visser Wiesje M. van der Flier Alzheimer Center Amsterdam Department of Neurology Vrije Universiteit Amsterdam Amsterdam UMC location VUmc Amsterdam the Netherlands Amsterdam Neuroscience Neurodegeneration Amsterdam the Netherlands Medical Psychology Amsterdam UMC location AMC University of Amsterdam Amsterdam the Netherlands Amsterdam Public Health Quality of Care Amsterdam the Netherlands Amsterdam Public Health Ageing and later life Amsterdam the Netherlands Amsterdam Public Health Personalized Medicine Amsterdam the Netherlands Division of Clinical Geriatrics Center for Alzheimer Research Department of Neurobiology Care Sciences and Society Karolinska Institutet Stockholm Sweden Department of Bioethics and Health Humanities Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht University Utrecht the Netherlands Epidemiology and Data Science Vrije Universiteit Amsterdam Amsterdam UMC Amsterdam the Netherlands

Background We examined how patients evaluated the information provided during their diagnostic trajectory in a tertiary memory clinic. Methods Between February 2021 and June 2023, 802 patients of Alzheimer center Amsterdam completed a questionnaire assessing satisfaction with the amount and perceived helpfulness of information (1–4 point Likert scale), wish for more/less information (yes/no), and which information was missed/redundant (open-ended). We tested effects of age, sex, education, syndrome- and etiological diagnosis, Mini-Mental State Examination (MMSE), and time since diagnostic trajectory. Results Patients were satisfied with the amount (3.1 ± 0.8) and helpfulness (3.1 ± 0.9) of information. Satisfaction was highest among those who recently underwent the diagnostic trajectory (decline over time p < 0.001). One quarter (214/802 [27%]) wished for more information, particularly those with higher MMSE scores ( p = 0.01), mainly concerning specific test results and prognosis. Few (18/802 [2%]) preferred less information. Discussion Our results highlight the importance of providing sufficient and tailored information, as well as recurrently checking information needs over time.

关键词： Alzheimer's disease clinician–patient communication dementia information provision memory clinic mild cognitive impairment patient-reported experiences patient-reported outcomes satisfaction subjective cognitive decline

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The effects of different travel modes and travel destinations on COVID-19 transmission in global cities

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science Bulletin 2022年第6期67卷 588-592,M0003页

作者： Rui Zhu Luc Anselin Michael Batty Mei-Po Kwan Min Chen Wei Luo Tao Cheng Che Kang Lim Paolo Santi Cheng Cheng Qiushi Gu Man Sing Wong Kai Zhang Guonian Lü Carlo Ratti Department of Land Surveying and Geo-Informatics the Hong Kong Polytechnic UniversityHong KongChina Key Laboratory of Virtual Geographic Environment(Ministry of Education) Nanjing Normal UniversityNanjing 210023China Senseable City Laboratory Future Urban Mobility IRGSingapore-MIT Alliance for Research and TechnologySingapore 138602Singapore Center for Spatial Data Science Division of the Social SciencesUniversity of ChicagoChicago IL 60637USA School of Geography Science Nanjing Normal UniversityNanjing 210023China Centre for Advanced Spatial Analysis Faculty of the Built EnvironmentUniversity College LondonLondon WC1E 6BTUK Department of Geography and Resource Management the Chinese University of Hong KongHong KongChina Institute of Space and Earth Information Science the Chinese University of Hong KongHong KongChina Department of Human Geography and Spatial Planning Utrecht UniversityUtrecht 3584 CSNetherlands State Key Laboratory Cultivation Base of Geographical Environment Evolution Nanjing 210023China Department of Geography National University of SingaporeSingapore 117570Singapore SpaceTimeLab Department of CivilEnvironmental and Geomatic EngineeringUniversity College LondonLondon WC1E 6BTUK Department of Clinical Translational Research Singapore General HospitalSingapore 169856Singapore Division of Clinical Immunology and Transfusion Medicine Department of Laboratory MedicineKarolinska InstitutetStockholm 17177Sweden Senseable City Laboratory Department of Urban Studies and PlanningMassachusetts Institute of TechnologyCambridgeMA 02139USA Istituto di Informatica e Telematica del CNR Pisa 56124Italy Department of System Science Institute of High Performance Computing Agency for ScienceTechnology and ResearchSingapore 138632Singapore Department of Tourism Studies School of HumanitiesSoutheast UniversityNanjing 210096China

The international community has made significant efforts to flatten the COVID-19 curve,including predicting transmission[1,2],executing unprecedented global lockdowns and social distancing[3,4],promoting the wearing of facemasks and social distancing measures[5],and isolating confirmed cases and contacts[6].Because of the adverse consequences of these lockdown measures[7],many cities have reopened so they can rebuild their ***,as mobility has gradually returned towards normal,imported cases from unknown sources have disrupted the recovery situation,and cities are continually at high risk of new waves of infection[8,9]since airborne transmission is the dominant transmission route[10].

关键词：多变量时间序列疫情传播出行方式谷歌公司交通出行病毒传播出行行为大城市

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Correction: Random forest algorithms to classify frailty and falling history in seniors using plantar pressure measurement insoles: a large-scale feasibility study

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BMC geriatrics 2022年第1期22卷 946页

作者： Emi Anzai Dian Ren Leo Cazenille Nathanael Aubert-Kato Julien Tripette Yuji Ohta Faculty of Engineering Nara Women's University Nara Japan. Department of Cooperative Major in Human Centered Engineering Graduate School of Humanities and Sciences Ochanomizu University Tokyo Japan. Department of Information Sciences Ochanomizu University Tokyo Japan. Center for Interdisciplinary AI and Data Science Ochanomizu University Tokyo Japan. Center for Interdisciplinary AI and Data Science Ochanomizu University Tokyo Japan. tripette.julien@ocha.ac.jp. Department of Human-Environmental Science Faculty of Human Life and Environmental Sciences Ochanomizu University Tokyo Japan. tripette.julien@ocha.ac.jp. Department of Human-Environmental Science Faculty of Human Life and Environmental Sciences Ochanomizu University Tokyo Japan. Faculty of Core Research Natural Science Division Ochanomizu University Tokyo Japan.

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Empirical assessment of bias in machine learning diagnostic test accuracy studies

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Journal of AOAC International 2020年第6期103卷 1092-1101页

作者： Crowley, Ryan J. Tan, Yuan Jin Ioannidis, John P.A. Meta-Research Innovation Center at Stanford Stanford University StanfordCA United States Department of Bioengineering Stanford School of Engineering Stanford University StanfordCA United States Department of Epidemiology and Population Health Stanford University School of Medicine StanfordCA United States Stanford Prevention Research Center Department of Medicine Stanford Medicine Stanford University StanfordCA United States Department of Biomedical Data Science Stanford Medicine Stanford University StanfordCA United States Department of Statistics School of Humanities and Science Stanford University StanfordCA United States

Objective: Machine learning (ML) diagnostic tools have significant potential to improve health care. However, methodological pitfalls may affect diagnostic test accuracy studies used to appraise such tools. We aimed to evaluate the prevalence and reporting of design characteristics within the literature. Further, we sought to empirically assess whether design features may be associated with different estimates of diagnostic accuracy. Materials and Methods: We systematically retrieved 2 _ 2 tables (n 281) describing the performance of ML diagnostic tools, derived from 114 publications in 38 meta-analyses, from PubMed. data extracted included test performance, sample sizes, and design features. A mixed-effects metaregression was run to quantify the association between design features and diagnostic accuracy. Results: Participant ethnicity and blinding in test interpretation was unreported in 90% and 60% of studies, respectively. Reporting was occasionally lacking for rudimentary characteristics such as study design (28% unreported). Internal validation without appropriate safeguards was used in 44% of studies. Several design features were associated with larger estimates of accuracy, including having unreported (relative diagnostic odds ratio [RDOR], 2.11;95% confidence interval [CI], 1.43-3.1) or case-control study designs (RDOR, 1.27;95% CI, 0.97- 1.66), and recruiting participants for the index test (RDOR, 1.67;95% CI, 1.08-2.59). Discussion: Significant underreporting of experimental details was present. Study design features may affect estimates of diagnostic performance in the ML diagnostic test accuracy literature. Conclusions: The present study identifies pitfalls that threaten the validity, generalizability, and clinical value of ML diagnostic tools and provides recommendations for improvement. © The Author(s) 2020.

关键词： Machine learning

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Reward Processing in Novelty Seekers: A Transdiagnostic Psychiatric Imaging Biomarker

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BIOLOGICAL PSYCHIATRY 2021年第8期90卷 529-539页

作者： Qi, Shile Schumann, Gunter Bustillo, Juan Turner, Jessica A. Jiang, Rongtao Zhi, Dongmei Fu, Zening Mayer, Andrew R. Vergara, Victor M. Silva, Rogers F. Iraji, Armin Chen, Jiayu Damaraju, Eswar Ma, Xiaohong Yang, Xiao Stevens, Michael Mathalon, Daniel H. Ford, Judith M. Voyvodic, James Mueller, Bryon A. Belger, Aysenil Potkin, Steven G. Preda, Adrian Zhuo, Chuanjun Xu, Yong Chu, Congying Banaschewski, Tobias Barker, Gareth J. Bokde, Arun L. W. Quinlan, Erin Burke Desrivieres, Sylvane Flor, Herta Grigis, Antoine Garavan, Hugh Gowland, Penny Heinz, Andreas Martinot, Jean-Luc Martinot, Marie-Laure Paillere Artiges, Eric Nees, Frauke Orfanos, Dimitri Papadopoulos Paus, Tomas Poustka, Luise Hohmann, Sarah Frohner, Juliane H. Smolka, Michael N. Walter, Henrik Whelan, Robert Calhoun, Vince D. Sui, Jing [a]Tri-institutional Center for Translational Research in Neuroimaging and Data Science Georgia State University Georgia Institute Technology and Emory University Atlanta Georgia [b]Department of Psychiatry University of New Mexico Albuquerque New Mexico [c]Department of Psychology Georgia State University Atlanta Georgia [d]Olin Neuropsychiatry Research Center Hartford Connecticut [e]Department of Psychiatry University of California San Francisco San Francisco California [f]Department of Radiology Duke University Durham North Carolina [g]Department of Psychiatry University of North Carolina at Chapel Hill Chapel Hill North Carolina [h]Department of Psychiatry University of Minnesota Minneapolis Minnesota [i]Department of Psychiatry University of California Irvine Irvine California [j]Departments of Psychiatry and Psychology University of Vermont Burlington Vermont [k]Centre for Population Neuroscience and Stratified Medicine Institute for Science and Technology of Brain-Inspired Intelligence Fudan University Shanghai China [l]University of Chinese Academy of Sciences Beijing China [dd]Department of Computer Science and Engineering Nanjing University of Aeronautics and Astronautics Nanjing China [m]Brainnetome Center and National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China [ee]State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University Beijing China [n]Psychiatric Laboratory and Mental Health Center the State Key Laboratory of Biotherapy West China Hospital of Sichuan University Chengdu China [o]Department of Psychiatric-Neuroimaging-Genetics and Morbidity Laboratory Nankai University Affiliated Anding Hospital Tianjin China [p]Department of Humanities and Social Science Shanxi Medical University Taiyuan China [q]Department of Psychiatry and Psychotherapy Charité – Universitätsmedizin Berlin Campus Charité Mitte corporate member of Freie Universität Berlin Humboldt-Univers

BACKGROUND: Dysfunctional reward processing is implicated in multiple mental disorders. Novelty seeking (NS) assesses preference for seeking novel experiences, which is linked to sensitivity to reward environmental cues. METHODS: A subset of 14-year-old adolescents (IMAGEN) with the top 20% ranked high-NS scores was used to identify high-NS-associated multimodal components by supervised fusion. These features were then used to longitudinally predict five different risk scales for the same and unseen subjects (an independent dataset of subjects at 19 years of age that was not used in predictive modeling training at 14 years of age) (within IMAGEN, n z 1100) and even for the corresponding symptom scores of five types of patient cohorts (non-IMAGEN), including drinking (n = 313), smoking (n = 104), attention-deficit/hyperactivity disorder (n = 320), major depressive disorder (n = 81), and schizophrenia (n = 147), as well as to classify different patient groups with diagnostic labels. RESULTS: Multimodal biomarkers, including the prefrontal cortex, striatum, amygdala, and hippocampus, associated with high NS in 14-year-old adolescents were identified. The prediction models built on these features are able to longitudinally predict five different risk scales, including alcohol drinking, smoking, hyperactivity, depression, and psychosis for the same and unseen 19-year-old adolescents and even predict the corresponding symptom scores of five types of patient cohorts. Furthermore, the identified reward-related multimodal features can classify among attention-deficit/hyperactivity disorder, major depressive disorder, and schizophrenia with an accuracy of 87.2%. CONCLUSIONS: Adolescents with higher NS scores can be used to reveal brain alterations in the reward-related system, implicating potential higher risk for subsequent development of multiple disorders. The identified high-NS- associated multimodal reward-related signatures may serve as a transdiagnostic neuroimaging bi

关键词： ADHD Attention-deficit/hyperactivity disorder Major depressive disorders MDD Novelty seeking Reward processing Schizophrenia Substance use

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Environment scan of generative AI infrastructure for clinical and translational science

Npj health systems

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Npj health systems 2025年第1期2卷 4页

作者： Betina Idnay Zihan Xu William G Adams Mohammad Adibuzzaman Nicholas R Anderson Neil Bahroos Douglas S Bell Cody Bumgardner Thomas Campion Mario Castro James J Cimino I Glenn Cohen David Dorr Peter L Elkin Jungwei W Fan Todd Ferris David J Foran David Hanauer Mike Hogarth Kun Huang Jayashree Kalpathy-Cramer Manoj Kandpal Niranjan S Karnik Avnish Katoch Albert M Lai Christophe G Lambert Lang Li Christopher Lindsell Jinze Liu Zhiyong Lu Yuan Luo Peter McGarvey Eneida A Mendonca Parsa Mirhaji Shawn Murphy John D Osborne Ioannis C Paschalidis Paul A Harris Fred Prior Nicholas J Shaheen Nawar Shara Ida Sim Umberto Tachinardi Lemuel R Waitman Rosalind J Wright Adrian H Zai Kai Zheng Sandra Soo-Jin Lee Bradley A Malin Karthik Natarajan W Nicholson Price Ii Rui Zhang Yiye Zhang Hua Xu Jiang Bian Chunhua Weng Yifan Peng Department of Biomedical Informatics Columbia University Irving Medical Center New York NY USA. Department of Population Health Sciences Weill Cornell Medicine New York NY USA. Department of Pediatrics Boston Medical Center Boston MA USA Chobanian & Avedisian School of Medicine Boston University Boston MA USA. Oregon Clinical and Translational Research Institute Oregon Health and Science University Portland OR USA. Department of Public Health Sciences University of California Davis Davis CA USA. Keck School of Medicine University of Southern California Los Angeles CA USA. Department of Medicine David Geffen School of Medicine University of California Los Angeles Los Angeles CA USA. Department of Pathology and Laboratory Medicine University of Kentucky College of Medicine Lexington KY USA. Clinical and Translational Science Center Weill Cornell Medicine New York NY USA. Division of Pulmonary Critical Care and Sleep Medicine University of Kansas School of Medicine Kansas City KS USA. Department of Biomedical Informatics and Data Science Heersink School of Medicine University of Alabama Birmingham AL USA. Harvard Law School Petrie-Flom Center for Health Law Policy Biotechnology and Bioethics Harvard University Cambridge MA USA. Department of Biomedical Informatics University at Buffalo Buffalo NY USA. Center for Clinical and Translational Science Mayo Clinic Rochester MN USA. Technology and Digital Solutions Stanford Medicine Stanford University Stanford CA USA. Center for Biomedical Informatics Rutgers Cancer Institute New Brunswick NJ USA. Department of Learning Health Sciences University of Michigan Medical School Ann Arbor MI USA. Altman Clinical and Translational Research Institute (ACTRI) University of California San Diego La Jolla CA USA. Department of Biostatistics and Health Data Science School of Medicine Indiana University Indianapolis IN USA. Department of Ophthalmology CCTSI University of Colorado Aurora CO USA. Center for Clinic

This study reports a comprehensive environmental scan of the generative AI (GenAI) infrastructure in the national network for clinical and translational science across 36 institutions supported by the CTSA Program led by the National center for Advancing Translational sciences (NCATS) of the National Institutes of Health (NIH) at the United States. Key findings indicate a diverse range of institutional strategies, with most organizations in the experimental phase of GenAI deployment. The results underscore the need for a more coordinated approach to GenAI governance, emphasizing collaboration among senior leaders, clinicians, information technology staff, and researchers. Our analysis reveals that 53% of institutions identified data security as a primary concern, followed by lack of clinician trust (50%) and AI bias (44%), which must be addressed to ensure the ethical and effective implementation of GenAI technologies.

关键词： Health services Public health

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An overview on visualization of ontology alignment and ontology entity 5th

An overview on visualization of ontology alignment and ontol...

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5th Euro-China Conference on Intelligent data Analysis and Applications, ECC 2018

作者： Chen, Jie Xue, Xingsi Huang, Lili Ren, Aihong College of Information Science and Engineering Fujian University of Technology FuzhouFujian350118 China Intelligent Information Processing Research Center Fujian University of Technology FuzhouFujian350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications FuzhouFujian350118 China Fujian Key Lab for Automotive Electronics and Electric Drive Fujian University of Technology FuzhouFujian350118 China College of Humanities and Law Fujian Agriculture and Forestry University FuzhouFujian350002 China School of Mathematics and Information Science Baoji University of Arts and Sciences BaojiShaanxi721013 China

ISBN: (纸本)9783030037659

Visualization of ontology alignment and ontology entity is becoming a method to help Solve the problem of semantic heterogeneity. This paper first introduces the basic concepts of ontology and ontology matching, and then illustrates the importance of ontology visualization technique. The existing partial visualization tools are discussed respectively in two aspects of visualization of ontology alignment and visualization entity on ontology. Finally, four researching directions on ontology visualization technique are pointed out for the future work. © Springer Nature Switzerland AG 2019.

关键词： Ontology

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