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Correction: An Alternative to the Light Touch Digital Health Remote Study: The Stress and Recovery in Frontline COVID-19 Health Care Workers Study

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JMIR formative research 2022年第4期6卷 e38188页

作者： Sarah M Goodday Emma Karlin Alexandria Alfarano Alexa Brooks Carol Chapman Rachelle Desille Shazia Rangwala Daniel R Karlin Hoora Emami Nancy Fugate Woods Adrien Boch Luca Foschini Mackenzie Wildman Francesca Cormack Nick Taptiklis Abhishek Pratap Marzyeh Ghassemi Anna Goldenberg Sujay Nagaraj Elaine Walsh Stephen Friend 4YouandMe Seattle WA United States. Department of Psychiatry University of Oxford Oxford United Kingdom. Nationwide Children's Hospital Columbus OH United States. MindMed New York NY United States. Tufts University School of Medicine Boston MA United States. Dalla Lana School of Public Health University of Toronto Toronto ON Canada. School of Nursing University of Washington Seattle WA United States. Evidation Health Inc San Mateo CA United States. Cambridge Cognition Cambridge United Kingdom. Department of Psychiatry University of Cambridge Cambridge United Kingdom. Krembil Center for Neuroinformatics Center for Addiction and Mental Health Toronto ON Canada. Vector Institute Toronto ON Canada. University of Washington Seattle WA United States. King's College London London United Kingdom. Institute for Medical Engineering and Science MIT Cambridge MA United States. Department of Electrical Engineering and Computer Science MIT Cambridge MA United States. The Hospital for Sick Children Toronto ON Canada. Department of Computer Science University of Toronto Toronto ON Canada. Canadian Institute for Advanced Research Toronto ON Canada.

[This corrects the article DOI: 10.2196/32165.].

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Single-view food portion estimation: Learning image-to-energy mappings using generative adversarial networks

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arXiv 2018年

作者： Fang, Shaobo Shao, Zeman Mao, Runyu Fu, Chichen Kerr, Deborah A. Boushey, Carol J. Delp, Edward J. Zhu, Fengqing School of Electrical and Computer Engineering Purdue University West LafayetteIN United States School of Public Health Curtin University PerthWA Australia Cancer Epidemiology Program University of Hawaii Cancer Center HonoluluHI United States

Due to the growing concern of chronic diseases and other health problems related to diet, there is a need to develop accurate methods to estimate an individual's food and energy intake. Measuring accurate dietary intake is an open research problem. In particular, accurate food portion estimation is challenging since the process of food preparation and consumption impose large variations on food shapes and appearances. In this paper, we present a food portion estimation method to estimate food energy (kilocalories) from food images using Generative Adversarial Networks (GAN). We introduce the concept of an "energy distribution" for each food image. To train the GAN, we design a food image dataset based on ground truth food labels and segmentation masks for each food image as well as energy information associated with the food image. Our goal is to learn the mapping of the food image to the food energy. We can then estimate food energy based on the energy distribution. We show that an average energy estimation error rate of 10.89% can be obtained by learning the image-to-energy mapping. Copyright © 2018, The Authors. All rights reserved.

关键词： Mapping

All models are wrong, but many are useful: Learning a variable's importance by studying an entire class of prediction models simultaneously

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arXiv 2018年

作者： Fisher, Aaron Rudin, Cynthia Dominici, Francesca Takeda Pharmaceuticals CambridgeMA02139 United States Departments of Computer Science and Electrical and Computer Engineering Duke University DurhamNC27708 United States Department of Biostatistics Harvard T.H. Chan School of Public Health BostonMA02115 United States

Variable importance (VI) tools describe how much covariates contribute to a prediction model's accuracy. However, important variables for one well-performing model (for example, a linear model f(x) = xTβ with a fixed coefficient vector β) may be unimportant for another model. In this paper, we propose model class reliance (MCR) as the range of VI values across all well-performing model in a prespecified class. Thus, MCR gives a more comprehensive description of importance by accounting for the fact that many prediction models, possibly of different parametric forms, may fit the data well. In the process of deriving MCR, we show several informative results for permutation-based VI estimates, based on the VI measures used in Random Forests. Specifically, we derive connections between permutation importance estimates for a single prediction model, U-statistics, conditional variable importance, conditional causal effects, and linear model coefficients. We then give probabilistic bounds for MCR, using a novel, generalizable technique. We apply MCR to a public data set of Broward County criminal records to study the reliance of recidivism prediction models on sex and race. In this application, MCR can be used to help inform VI for unknown, proprietary models. Copyright © 2018, The Authors. All rights reserved.

关键词： Forecasting

A conceptual model of an electronic appendix called "nursing file for palliative care", for training students from the speciality of nurse 17

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A conceptual model of an electronic appendix called "nursing...

18th International Conference on computer Systems and Technologies, CompSysTech 2017

作者： Koleva, Greta Georgieva, Despina Stefanov, Yavor Faculty of Public Health and Health Care Department of Health Care University of Ruse Bulgaria Faculty of Electrical Engineering Electronics and Automatics Department of Computer Systems and Technologies University of Ruse Bulgaria

ISBN: (纸本)9781450352345

Globally, nursing as a science is dynamically developing, in response to the growing needs of health care. The documentation of the performed activities and interventions by the nurse, is an important element of her work and a specific moment of the training process of the field of study. The suggested conceptual method of Electronic nursing file, gives opportunity to systematize and optimize the training process, and also responds to the digital generations' expectations. It creates conditions for interactive training, for a timely reaction by the student, as well as a fair estimation of the achieved skills, knowledge and competence. © 2017 Association for Computing Machinery.

关键词： Nursing

Percutaneous Coronary Intervention With a Drug-Eluting Stent Versus Coronary Artery Bypass Grafting in Patients Receiving Dialysis: A National Study From Taiwan

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Kidney medicine 2024年第2期6卷 100768页

作者： Szu-Yu Pan Ju-Yeh Yang Nai-Chi Teng Yun-Yi Chen Shi-Heng Wang Chien-Lin Lee Kang-Lung Chen Yen-Ling Chiu Shih-Ping Hsu Yu-Sen Peng Yung-Ming Chen Shuei-Liong Lin Likwang Chen Department of Integrated Diagnostics and Therapeutics National Taiwan University Hospital Taipei Taiwan. Division of Nephrology Department of Internal Medicine National Taiwan University Hospital Taipei Taiwan. Division of Nephrology Department of Internal Medicine Far Eastern Memorial Hospital New Taipei City Taiwan. Institute of Health Policy and Management College of Public Health National Taiwan University Taipei Taiwan. Institute of Population Health Sciences National Health Research Institutes Zhunan Taiwan. Department of Research Shin-Kong Wu Ho-Su Memorial Hospital Taipei Taiwan. Institute of Hospital and Health Care Administration National Yang Ming Chiao Tung University Taipei Taiwan. National Center for Geriatrics and Welfare Research National Health Research Institutes Yunlin Taiwan. Department of Cardiology Far Eastern Memorial Hospital New Taipei City Taiwan. Department of Cardiovascular Surgery Far Eastern Memorial Hospital New Taipei City Taiwan. Graduate Program in Biomedical Informatics Department of Computer Science and Engineering College of Informatics Yuan Ze University Taoyuan Taiwan. Graduate Institute of Clinical Medicine National Taiwan University College of Medicine Taipei Taiwan. Graduate Institute of Physiology College of Medicine National Taiwan University Taipei Taiwan.

Rationale & Objective:We aimed to study the comparative effectiveness of percutaneous coronary intervention with drug-eluting stent and coronary artery bypass grafting in patients receiving dialysis. Study Design:This was a retrospective observational cohort study. Setting & Participants:This population-based study identified patients receiving dialysis hospitalized for coronary revascularization between January 1, 2009 and December 31, 2015, in the Taiwan National Health Insurance Research Database. Exposures:Patients received percutaneous coronary intervention with drug-eluting stent versus coronary artery bypass grafting. Outcomes:The study outcomes were all-cause mortality, in-hospital mortality, and repeat revascularization. Analytical Approach:Propensity scores were used to match patients. Cox proportional hazards models and logistic regression models were constructed to examine associations between revascularization strategies and mortality. Interval Cox models were fitted to estimate time-varying hazards during different periods. Results:A total of 1,840 propensity score-matched patients receiving dialysis were analyzed. Coronary artery bypass grafting was associated with higher in-hospital mortality (coronary artery bypass grafting vs percutaneous coronary intervention with drug-eluting stent; crude mortality rate 12.5% vs 3.3%; adjusted OR, 5.22; 95% CI, 3.42-7.97; < 0.001) and longer hospitalization duration (median [IQR], 20 [14-30] days vs 3 [2-8] days; < 0.001). After discharge, repeat revascularization, acute coronary syndrome, and repeat hospitalization all occurred more frequently in the percutaneous coronary intervention with drug-eluting stent group. Importantly, with a median follow-up of 2.8 years, coronary artery bypass grafting was significantly associated with a higher risk of all-cause overall mortality (adjusted HR, 1.19; 95% CI, 1.05-1.35; = 0.006) in the multivariable Cox proportional hazard model. Sensitivity and subgroup analyses yie

关键词： Coronary artery bypass grafting coronary revascularization dialysis drug-eluting stent end-stage kidney disease mortality percutaneous coronary intervention survival

Consensus Recommendations for Hyperpolarized [1‐13C]pyruvate MRI Multi-center Human Studies

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arXiv 2025年

作者： Punwani, Shonit Larson, Peder E.Z. Laustsen, Christoffer VanderMeulen, Jan Ardenkjær-Larsen, Jan Henrik Autry, Adam W. Bankson, James A. Bernard, Jenna Bok, Robert Bertelsen, Lotte Bonde Che, Jenny Chen, Albert P. Chowdhury, Rafat Comment, Arnaud Cunningham, Charles H. Dang, Duy Gallagher, Ferdia A. Gaunt, Adam Gong, Yangcan Gordon, Jeremy W. Grimmer, Ashley Grist, James Hansen, Esben Søvsø Szocska Lerche, Mathilde Hauge Hesketh, Richard L. Hoevener, Jan-Bernd Hsieh, Ching-Yi Keshari, Kayvan R. Kozerke, Sebastian Lanz, Titus Mayer, Dirk McLean, Mary Park, Jae Mo Slater, Jim Tyler, Damian Vanderheyden, Jean-Luc von Morze, Cornelius Zaccagna, Fulvio Zaha, Vlad Xu, Duan Vigneron, Daniel Department of Radiology University College London Hospital NHS Foundation Trust London United Kingdom Centre for Medical Imaging University College London London United Kingdom Department of Radiology and Biomedical Imaging University of California San FranciscoCA94143 United States The MR Research Center Department of Clinical Medicine Aarhus University Aarhus Denmark Department of Health Services Research & Policy London School of Hygiene & Tropical Medicine London United Kingdom Department of Imaging Physics The University of Texas MD Anderson Cancer Center HoustonTX United States GE Healthcare TorontoON Canada GE HealthCare Pollards Wood Nightingales Lane Chalfont St Giles BuckinghamshireHP8 4SP United Kingdom Physical Sciences Sunnybrook Research Institute TorontoON Canada Department of Medical Biophysics University of Toronto TorontoON Canada Department of Radiology University of Cambridge Cambridge University Hospitals NHS Foundation Trust Cambridge United Kingdom GE Healthcare San FranciscoCA United States Oxford Centre for Clinical Magnetic Resonance Research University of Oxford Oxford United Kingdom Department of Health Technology Technical University of Denmark Kgs. Lyngby2800 Denmark Cancer Research UK Cambridge Institute University of Cambridge Cambridge United Kingdom Section Biomedical Imaging University Hospital Schleswig-Holstein Kiel University Campus Kiel Germany Research Center for Radiation Medicine Chang Gung University Taoyuan Taiwan Department of Medical Imaging and Intervention Chang Gung Memorial Hospital at Linkou Taoyuan Taiwan Department of Radiology Memorial Sloan Kettering Cancer Center New YorkNY United States Molecular Pharmacology Program Memorial Sloan Kettering Cancer Center New YorkNY United States Institute for Biomedical Engineering University and ETH Zurich Zurich Switzerland RAPID Biomedical GmbH Rimpar97222 Germany Department of Diagnostic Radiology and Nuclear Medicine University of Mary

Magnetic resonance imaging of hyperpolarized (HP) [1-13C]pyruvate allows in-vivo assessment of metabolism and has translated into human studies across diseases at 15 centers worldwide. Consensus on best practice for multi-center studies is required to develop clinical applications. This paper presents the results of a 2-round formal consensus building exercise carried out by experts with HP [1-13C]pyruvate human study experience. Twenty-nine participants from 13 sites brought together expertise in pharmacy methods, MR physics, translational imaging, and data-analysis;with the goal of providing recommendations and best practice statements on conduct of multi-center human studies of HP [1-13C]pyruvate MRI. Overall, the group reached consensus on approximately two-thirds of 246 statements in the questionnaire, covering ‘HP 13C-Pyruvate Preparation’, ‘MRI System Setup, Calibration, and Phantoms’, ‘Acquisition and Reconstruction’, and ‘Data Analysis and Quantification’. Consensus was present across categories, examples include that: (i) different HP pyruvate preparation methods could be used in human studies, but that the same release criteria have to be followed;(ii) site qualification and quality assurance must be performed with phantoms and that the same field strength must be used, but that the rest of the system setup and calibration methods could be determined by individual sites;(iii) the same pulse sequence and reconstruction methods were preferable, but the exact choice should be governed by the anatomical target;(iv) normalized metabolite area-under-curve (AUC) values and metabolite AUC were the preferred metabolism metrics. The work confirmed areas of consensus for multi-center study conduct and identified where further research is required to ascertain best practice. © 2025, CC BY.

关键词： Redundancy

Unlocking capacities of viral genomics for the COVID-19 pandemic response

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arXiv 2021年

作者： Knyazev, Sergey Chhugani, Karishma Sarwal, Varuni Ayyala, Ram Singh, Harman Karthikeyan, Smruthi Deshpande, Dhrithi Comarova, Zoia Lu, Angela Porozov, Yuri Wu, Aiping Abedalthagafi, Malak S. Nagaraj, Shivashankar H. Smith, Adam L. Skums, Pavel Ladner, Jason Lam, Tommy Tsan-Yuk Wu, Nicholas C. Zelikovsky, Alex Knight, Rob Crandall, Keith A. Mangul, Serghei Department of Computer Science College of Art and Science Georgia State University 1 Park Place AtlantaGA30303 United States Department of Pharmacology and Pharmaceutical Sciences School of Pharmacy University of Southern California 1985 Zonal Avenue Los AngelesCA90089 United States Department of Computer Science University of California Los Angeles 580 Portola Plaza Los AngelesCA90095 United States Department of Neuroscience College of Life Sciences University of California Los Angeles 580 Portola Plaza Los AngelesCA90095 United States Department of Electrical Engineering Indian Institute of Technology Hauz Khas New Delhi110016 India Department of Pediatrics University of California San Diego San DiegoCA United States Paradigm Environmental 3911 Old Lee Highway FairfaxVA22030 United States Department of Pharmacology and Pharmaceutical Sciences School of Pharmacy University of Southern California 1985 Zonal Avenue Los AngelesCA90089-9121 United States World-Class Research Center Digital Biodesign and Personalized Healthcare I.M. Sechenov First Moscow State Medical University Moscow Russia Department of Computational Biology Sirius University of Science and Technology Sochi Russia Center for Systems Medicine Institute of Basic Medical Sciences Chinese Academy of Medical Sciences Peking Union Medical College Beijing100005 China Suzhou Institute of Systems Medicine Suzhou215123 China Genomics Research Department Saudi Human Genome Project King Fahad Medical City King Abdulaziz City for Science and Technology Riyadh Saudi Arabia Centre for Genomics and Personalised Health Queensland University of Technology BrisbaneQLD4059 Australia Translational Research Institute Brisbane Australia Astani Department of Civil and Environmental Engineering University of Southern California 3620 South Vermont Avenue Los AngelesCA90089 United States Department of Computer Science College of Art and Science Georgia State University 1 Park Place Atlanta

More than any other infectious disease epidemic, the COVID-19 pandemic has been characterized by the generation of large volumes of viral genomic data at an incredible pace due to recent advances in high-throughput sequencing technologies, the rapid global spread of SARS-CoV-2, and its persistent threat to public health. However, distinguishing the most epidemiologically relevant information encoded in these vast amounts of data requires substantial effort across the research and public health communities. Studies of SARS-CoV-2 genomes have been critical in tracking the spread of variants and understanding its epidemic dynamics, and may prove crucial for controlling future epidemics and alleviating significant public health burdens. Together, genomic data and bioinformatics methods enable broad-scale investigations of the spread of SARS-CoV-2 at the local, national, and global scales and allow researchers the ability to efficiently track the emergence of novel variants, reconstruct epidemic dynamics, and provide important insights into drug and vaccine development and disease control. Here, we discuss the tremendous opportunities that genomics offers to unlock the effective use of SARS-CoV-2 genomic data for efficient public health surveillance and guiding timely responses to COVID-19. © 2021, CC BY-NC-ND.

关键词： SARS

Political Affiliation and Rooftop Solar Adoption in New York and Texas

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Political Affiliation and Rooftop Solar Adoption in New York...

2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC)

作者： Deborah A. Sunter John Dees Sergio Castellanos Duncan Callaway Daniel M. Kammen Renewable and Appropriate Energy Laboratory University of California Berkeley CA USA Energy and Resources Group University of California Berkeley CA USA California Institute for Energy and Environment University of California Berkeley CA USA Department of Electrical Engineering and Computer Science University of California Berkeley CA USA Goldman School of Public Policy University of California Berkeley CA USA

Renewable energy is often presented as a partisan issue. However, solar incentives have made rooftop PV attractive to many consumers, regardless of political affiliation. This paper quantifies the role of political affiliation in rooftop PV adoption for the Democratic-leaning state of New York and Republican-leaning state of Texas. High resolution rooftop PV installation data from Google Project Sunroof is combined with election data from the Harvard Election Data Archive. We applied a bootstrapped LOWESS model to fit local linear relationships between rooftop PV adoption and both voting behavior and household income. In doing so, we find that Republican-majority communities are installing rooftop PV at equal or greater rates than Democraticmajority communities. While Republican politicians may be less likely than Democrats to support renewable energy, this partisan division is not found at the voter level.

关键词： Voting Renewable energy sources Portfolios Buildings Google Standards Indexes

Fast multilevel Lagrangian carotid strain imaging with GPU computing

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Fast multilevel Lagrangian carotid strain imaging with GPU c...

2017 IEEE International Ultrasonics Symposium, IUS 2017

作者： Meshram, Nirvedh Varghese, Tomy Department of Medical Physics University of Wisconsin School of Medicine and Public Health MadisonWI United States Department of Electrical and Computer Engineering University of Wisconsin Madison MadisonWI United States

ISBN: (纸本)9781538633830

Lagrangian carotid strain Imaging (LCSI) involves estimation of deformation in the arteries undergoing cardiac pulsation and tracking the local strain over a cardiac cycle which is computationally intensive. We incur long offline processing times for LCSI which is a limiting factor for clinical adoption. We report on computations gains for a parallelized implementation of LCSI our algorithm using CUDA programming for fast computation. © 2017 IEEE.

关键词： Graphics processing units Strain Estimation C++ languages Biomedical imaging