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25th Annual International Conference on Digital Government Research, DGO 2024

作者： Wong, Wilson Wong, Natalie Wai-Man Hinnant, Charles C. Data Science and Policy Studies Program Chinese University of Hong Kong Hong Kong Department of Public Administration National Chengchi University Taiwan School of Information Florida State University United States

ISBN: (纸本)9798400709883

Although there exists a growing body of literature exploring AI and its applications in the public sector, few explanatory theories have been developed. This paper aims to address this gap by examining the AI strategies of China and Taiwan through an analysis of their respective strategic documents. In this study, we find China has adopted a government-driven approach and Taiwan has pursued a public-private partnership approach, showcasing their own distinct characteristics and values. In contrast to predictions made in the existing literature on digital transformation the introduction of AI-driven changes has not led to a fundamental shift or breakthrough in the trajectory of the two public administration systems. Rather than leveraging AI for transformative purposes, governments primarily employ it to reinforce their current trajectory. This study highlights a theory-practice gap in the AI digital transformation of the public sector, emphasizing the need for theory-building rooted in the core theories and public values of public administration. © 2024 Copyright held by the owner/author(s).

关键词： Public administration

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Respiratory syncytial virus vaccine effectiveness among US veterans, September, 2023 to March, 2024: a target trial emulation study

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The Lancet Infectious Diseases 2025年第6期25卷 625-633页

作者： Bajema, Kristina L Yan, Lei Li, Yuli Argraves, Stephanie Rajeevan, Nallakkandi Fox, Alexandra Vergun, Robert Berry, Kristin Bui, David Huang, Yuan Lin, Hung-Mo Hynes, Denise M Lucero-Obusan, Cynthia Schirmer, Patricia Cunningham, Francesca Huang, Grant D Aslan, Mihaela Ioannou, George N Veterans Affairs Portland Health Care System Portland OR United States Division of Infectious Diseases Department of Medicine Oregon Health & Science University Portland OR United States Veterans Affairs Cooperative Studies Program Clinical Epidemiology Research Center Veterans Affairs Connecticut Health Care System West Haven CT United States Department of Biostatistics Yale School of Public Health New Haven CT United States Biomedical Informatics & Data Science Yale School of Medicine New Haven CT United States Department of Internal Medicine Yale School of Medicine New Haven CT United States Seattle Epidemiologic Research and Information Center Veterans Affairs Puget Sound Health Care System Seattle WA United States Research and Development Veterans Affairs Puget Sound Health Care System Seattle WA United States Center of Innovation to Improve Veteran Involvement in Care Veterans Affairs Portland Health Care System Portland OR United States Health Management and Policy College of Health Oregon State University Corvallis OR United States Health Data and Informatics Program Center for Quantitative Life Sciences Oregon State University Corvallis OR United States Public Health National Program Office Veterans Health Administration Palo Alto CA United States Public Health National Program Office Veterans Health Administration Washington DC United States Veterans Affairs Center for Medication Safety—Pharmacy Benefit Management Services Hines IL United States Office of Research and Development Veterans Health Administration Washington DC United States Division of Gastroenterology Veterans Affairs Puget Sound Health Care System and University of Washington Seattle WA United States

Background: New respiratory syncytial virus (RSV) vaccines have been approved in the USA for the prevention of RSV-associated lower respiratory tract disease in adults aged 60 years and older. Information on the real-world effectiveness of these vaccines is needed. Methods: We used electronic health records in the Veterans Health Administration to emulate a target trial comparing a single dose of a recombinant stabilised prefusion F protein RSV vaccine versus no vaccination among veterans aged 60 years and older. We matched eligible vaccine recipients with up to four unvaccinated individuals in four monthly nested sequential trials from Sept 1 to Dec 31, 2023. Outcomes were ascertained up to March 31, 2024. The primary outcome was any positive RSV test from day 14 following the matched index date. Secondary outcomes included hospitalisation and emergency department or urgent care encounter occurring within 1 day before or after a positive RSV test. We estimated vaccine effectiveness as 100 × (1 – risk ratio). Findings: We included 146 852 vaccinated individuals matched to 582 936 unique control individuals, weighted equally to represent 146 852 individuals. Across the two groups, 276 039 (94·0%) of 293 704 veterans were male, 17 665 (6·0%) were female, and median age was 75·9 years (IQR 71·7–79·7). Over a median follow-up of 124 days (IQR 102–150), the incidence rate of documented RSV infection was 1·7 (95% CI 1·4–2·1) events per 1000 person-years (88 total events) in the vaccinated group and 7·3 (6·6–8·1) per 1000 person-years in the unvaccinated group (372 total events), and vaccine effectiveness was estimated as 78·1% (72·6–83·5). Among the secondary outcomes, vaccine effectiveness was estimated at 78·7% (72·2–84·8) against RSV-associated emergency department or urgent care encounters, and 80·3% (65·8–90·1) against RSV-associated hospitalisation. Interpretation: RSV vaccination was effective in preventing RSV-related illness, including associated health-care use,

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Implementing electronic health record-based anxiety and depression screening in an epilepsy clinic: Theory-based implementation strategy and pre-post quantitative outcomes using RE-AIM

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Journal of Clinical and Translational science 2025年第1期9卷 e108-e108页

作者： Munger Clary, Heidi M. Alexander, Halley B. Gesell, Sabina Wan, Mingyu Conner, Kelly R. Boggs, Jane Robles, Christian Sam, Maria Christopher, Jerryl Marini, Christina Snively, Beverly M. Department of Neurology Wake Forest University School of Medicine United States Department of Social Sciences and Health Policy Wake Forest University School of Medicine United States Neuroscience Graduate Program Wake Forest University United States Department of Physician Assistant Studies Wake Forest University School of Medicine United States Department of Biostatistics and Data Science Wake Forest University School of Medicine United States Department of Neurology New York University Grossman School of Medicine United States

Introduction: Anxiety and depression in epilepsy are common and impactful. Screening with validated measures at every epilepsy visit is a quality measure, yet screening remains limited due to time constraints. Methods: This study aimed to develop an implementation strategy for anxiety and depression screening at an epilepsy center and evaluate it in a pre-post design with RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance). Guided by the Capability, Opportunity, Motivation-Behavior behavior change wheel framework, the strategy incorporated electronic health record tools and support staff activation of electronic screeners during visit check-in. Outcomes were evaluated over five months post-implementation and compared with two 3-month pre-implementation timeframes. Results: Post-implementation, 29.2% of 943 visits met the anxiety and depression screening quality measure, a significant increase from 12.6% immediately pre-implementation (p < 0.0001) and 6.28% before any screening interventions (p < 0.0001). Patients who completed electronic screeners post-implementation were younger than non-completers (mean 39.3 vs. 43.4 years, p = 0.001) and more likely to be white than other race/ethnicity categories (p = 0.002). There was substantial variability in screening rates among clinic staff (0-80% for support staff, 10.1-55.3% for providers), with higher screening among neurology support staff than temporary staff. Only 0.23% of post-implementation visits had screeners initiated but left incomplete. A shift to virtual visits during COVID-19 complicated Maintenance. Conclusions: This framework-based implementation strategy effectively increased screening rates by epilepsy specialists, though challenges remain, including variability across clinic team members and lower reach among older and non-white patients. This study describes a feasible strategy for epilepsy centers to use for improved performance on an American Academy of Neurology quality measure

关键词： epilepsy quality measures mental health Psychiatric comorbidity quality of life seizures

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Predictors of Co-occurring Cardiovascular and Gastrointestinal Disorders among Elderly with Osteoarthritis

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Osteoarthritis and Cartilage Open 2021年第2期3卷 100148-100148页

作者： Patel, Jayeshkumar Ladani, Amit Sambamoorthi, Nethra LeMasters, Traci Dwibedi, Nilanjana Sambamoorthi, Usha Department of Pharmaceutical Systems and Policy West Virginia University Morgantown United States Rheumatology West Virginia University Medicine Morgantown WV United States Masters in Data Science Program School of Professional Studies Northwestern University Chicago IL United States Department of Pharmacotherapy University of North Texas Health Sciences Center College of Pharmacy United States Vashisht Professor Health Disparities Health Education Awareness & Research in Disparities Scholar Texas Center for Health Disparities HSC United States

Objective: To identify the leading predictors of co-occurring cardiovascular or gastrointestinal disorders (CV-GID) in a real-world cohort of elderly with osteoarthritis (OA). Method: An observational retrospective cohort study using data from Optum's deidentified Clinformatics® data Mart was conducted. Elderly with OA were identified in 2015 and were followed for two years to identify co-occurring CV-GID including ischemic heart disease, stroke, heart failure, dyspepsia, gastroesophageal reflux disorder, and peptic ulcer disease. Random Forest (RF) and Partial Dependence Plots (PDP) were used to identify the leading predictors of CV-GID and to examine their associations. Multivariable logistic regression was also used to examine the association of the leading predictors with CV-GID. Results: Our study cohort consisted of 45,385 elderly with OA (mean age 76.0 years). CV-GID were present in 59% of elderly. Using RF, age was found to be the strongest predictor of CV-GID followed by cardiac arrhythmia, duration of opioid use, number of orthopedist or physical therapy visits, number of intra-articular corticosteroid injections, polypharmacy, duration of non-selective nonsteroidal anti-inflammatory drugs or oral corticosteroids, and hypertension. The PDPs demonstrated that higher age, cardiac arrhythmia, longer durations of opioid or oral corticosteroids, higher number of physical therapy visits or intra-articular corticosteroid use, polypharmacy, and hypertension were associated with a higher risk of CV-GID. Conclusion: CV-GIDs are common among elderly with OA and can be predicted based on certain clinical factors. Machine learning methods with PDPs can be used to improve the interpretability and inform decision-making. © 2021 The Author(s)

关键词： Cardiovascular disease Gastrointestinal disorders Machine learning Older adults Osteoarthritis Predictive modeling Random forest

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The science of Precision Prevention: Research Opportunities and Clinical Applications to Reduce Cardiovascular Health Disparities

JACC: Advances

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JACC: Advances 2024年第1期3卷 100759-100759页

作者： Pearson, Thomas A. Vitalis, Debbie Pratt, Charlotte Campo, Rebecca Armoundas, Antonis A. Au, David Beech, Bettina Brazhnik, Olga Chute, Christopher G. Davidson, Karina W. Diez-Roux, Ana V. Fine, Lawrence J. Gabriel, Davera Groenveld, Peter Hall, Jaclyn Hamilton, Alison B. Hu, Hui Ji, Heng Kind, Amy Kraus, William E. Krumholz, Harlan Mensah, George A. Merchant, Raina M. Mozaffarian, Dariush Murray, David M. Neumark-Sztainer, Dianne Petersen, Maya Goff, David College of Medicine and College of Public Health and Health Professions University of Florida Health Science Center Gainesville FL United States Division of Cardiovascular Sciences National Heart Lung and Blood Institute National Institutes of Health Bethesda MD United States Cardiovascular Research Center Massachusetts General Hospital and Broad Institute Massachusetts Institute of Technology Cambridge MA United States Center of Innovation for Veteran-Centered and Value-Driven Care University of Washington Seattle WA United States UH Population Health University of Houston Houston TX United States Johns Hopkins Medicine Institute for Clinical and Translational Research Baltimore MD United States Institute of Health System Science Feinstein Institutes for Medical Research Northwell Health New Hyde Park NY United States Feinstein Institutes for Medical Research Northwell Health Manhasset NY United States Urban Health Collaborative Drexel Dornsife School of Public Health Philadelphia PA United States Biomedical Informatics and Data Science Section Johns Hopkins University School of Medicine Baltimore MD United States Center for Health Care Transformation and Innovation University of Pennsylvania Perelman School of Medicine Philadelphia PA United States Department of Health Outcomes and Biomedical Informatics Institute for Child Health Policy College of Medicine University of Florida Gainesville FL United States Center for the Study of Healthcare Innovation Implementation & Policy VA Greater Los Angeles Healthcare System Los Angeles CA United States Channing Division of Network Medicine Department of Medicine Brigham and Women's Hospital and Harvard Medical School Boston MA United States Department of Computer Science University of Illinois Urbana-Champaign Champaign IL United States Center for Health Disparities Research (CHDR) University of Wisconsin School of Medicine and Public Health Madison WI United States Duke Molecular Physiology Insti

Precision prevention embraces personalized prevention but includes broader factors such as social determinants of health to improve cardiovascular health. The quality, quantity, precision, and diversity of data relatable to individuals and communities continue to expand. New analytical methods can be applied to these data to create tools to attribute risk, which may allow a better understanding of cardiovascular health disparities. Interventions using these analytic tools should be evaluated to establish feasibility and efficacy for addressing cardiovascular disease disparities in diverse individuals and communities. Training in these approaches is important to create the next generation of scientists and practitioners in precision prevention. This state-of-the-art review is based on a workshop convened to identify current gaps in knowledge and methods used in precision prevention intervention research, discuss opportunities to expand trials of implementation science to close the health equity gaps, and expand the education and training of a diverse precision prevention workforce. © 2023 The Authors

关键词： data science health equity health promotion implementation science personalized medicine precision analytics

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Author Correction: Communities conditionally support deployment of direct air capture for carbon dioxide removal in the United States

Communications Earth & Environment

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Communications Earth & Environment 2024年第1期5卷 1页

作者： Celina Scott-Buechler Bruce Cain Khalid Osman Nicole M. Ardoin Robert B. Jackson Catherine Fraser Grace Adcox Emily Polk Emmett Interdisciplinary Program in Environment & Resources (E-IPER) Stanford University Stanford CA USA Political Science Stanford University Stanford CA USA Civil and Environmental Engineering Stanford University Stanford CA USA Environmental Behavioral Sciences Stanford University Stanford CA USA Woods Institute for the Environment Stanford University Stanford CA USA Precourt Institute for Energy Stanford University Stanford CA USA McCourt School of Public Policy Georgetown University Washington DC USA Data for Progress Washington DC USA Writing and Rhetoric Studies Stanford University Stanford CA USA

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The 2022 report of the Lancet Countdown on health and climate change: health at the mercy of fossil fuels (vol 400, pg 1619, 2022)

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LANCET 2022年第10365期400卷 1766-1766页

作者： Romanello, M. Di Napoli, C. Drummond, P. Institute for Global Health University College London London UK School of Agriculture Policy and Development University of Reading Reading UK Institute for Sustainable Resources University College London London UK Department of Global Health Centre for Health and the Global Environment University of Washington Seattle WA USA UCL Energy Institute University College London London UK Department of Health Sciences University of York York UK Department of Meteorology University of Reading Reading UK Institute for Risk and Disaster Reduction University College London London UK School of Earth and Environment University of Leeds Leeds UK Centre on Climate Change and Planetary Health London School of Hygiene & Tropical Medicine London UK School of Population Health University of Melbourne Melbourne VIC Australia Department of Earth System Science Tsinghua University Beijing China Mercator Research Institute on Global Commons and Climate Change Berlin Germany Department of Environment Climate Change and Health World Health Organization Geneva Switzerland Institute of Environmental Sciences University of Geneva Geneva Switzerland Cardiovascular Epidemiology Unit Department of Public Health & Primary Care University of Cambridge Cambridge UK School of Government University of Birmingham Birmingham UK Economic Analysis of Climate Impacts and Policy Division Centro Euro-Mediterraneo sui Cambiamenti Climatici Venice Italy Institute for Environmental Design and Engineering University College London London UK Natural Resources Institute University of Greenwich London UK Department of Environmental Health Sciences and Yale Center on Climate Change and Health Yale University New Haven CT USA Department of Civil and Environmental Engineering Northeastern University Boston MA USA Department of Fish and Wildlife Conservation Virginia Polytechnic Institute and State University Blacksburg VA USA Department of Geography University College London London UK NU

With advancements in the science of detection and attribution studies, the influence of climate change over many events has now been quantified. Because of the rapidly increasing temperatures, vulnerable populations (adults older than 65 years, and children younger than one year of age) were exposed to 3·7 billion more heatwave days in 2021 than annually in 1986–2005 (indicator 1.1.2), and heat-related deaths increased by 68% between 2000–04 and 2017–21 (indicator 1.1.5), a death toll that was significantly exacerbated by the confluence of the COVID-19 pandemic. Despite the local cooling and overall health benefits of urban greenspaces, only 277 (27%) of 1038 global urban centres were at least moderately green in 2021 (indicator 2.2.3), and the number of households with air conditioning increased by 66% from 2000 to 2020, a maladaptive response that worsens the energy crisis and further increases urban heat, air pollution, and greenhouse gas emissions. [...]millions of people do not have access to the energy needed to keep their homes at healthy temperatures, preserve food and medication, and meet the seventh Sustainable Development Goal (to ensure access to affordable, reliable, sustainable, and modern energy for all). Aggravating this situation even further, governments continue to incentivise fossil fuel production and consumption: 69 (80%) of 86 countries reviewed had net-negative carbon prices (ie, provided a net subsidy to fossil fuels) for a net total of US$400 billion

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Search for K: Assessing Five Topic-Modeling Approaches to 120,000 Canadian Articles

Search for K: Assessing Five Topic-Modeling Approaches to 12...

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IEEE International Conference on Big data

作者： Qiang Fu Yufan Zhuang Jiaxin Gu Yushu Zhu Huihui Qin Xin Guo Department of Sociology The University of British Columbia Vancouver BC Canada Data Science Institute Columbia University New York NY USA Urban Studies Program and School of Public Policy Simon Fraser University Vancouver BC Canada Department of Applied Mathematics The Hong Kong Polytechnic University Hong Kong China

ISBN: (数字)9781728108582

ISBN: (纸本)9781728108599

Topic modeling has been an important field in natural language processing (NLP) and recently witnessed great methodological advances. Yet, the development of topic modeling is still, if not increasingly, challenged by two critical issues. First, despite intense efforts toward nonparametric/post-training methods, the search for the optimal number of topics K remains a fundamental question in topic modeling and warrants input from domain experts. Second, with the development of more sophisticated models, topic modeling is now ironically been treated as a black box and it becomes increasingly difficult to tell how research findings are informed by data, model specifications, or inference algorithms. Based on about 120,000 newspaper articles retrieved from three major Canadian newspapers (Globe and Mail, Toronto Star, and National Post) since 1977, we employ five methods with different model specifications and inference algorithms (Latent Semantic Analysis, Latent Dirichlet Allocation, Principal Component Analysis, Factor Analysis, Nonnegative Matrix Factorization) to identify discussion topics. The optimal topics are then assessed using three measures: coherence statistics, held-out likelihood (loss), and graph-based dimensionality selection. Mixed findings from this research complement advances in topic modeling and provide insights into the choice of optimal topics in social science research.

关键词： Principal component analysis Covariance matrices data models Matrix decomposition Loading Analytical models Semantics

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A future for the world's children? A WHO–UNICEF–Lancet Commission

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The Lancet 2020年第10224期395卷 605-658页

作者： Clark, Helen Coll-Seck, Awa Marie Banerjee, Anshu Peterson, Stefan Dalglish, Sarah L Ameratunga, Shanthi Balabanova, Dina Bhan, Maharaj Kishan Bhutta, Zulfiqar A Borrazzo, John Claeson, Mariam Doherty, Tanya El-Jardali, Fadi George, Asha S Gichaga, Angela Gram, Lu Hipgrave, David B Kwamie, Aku Meng, Qingyue Mercer, Raúl Narain, Sunita Nsungwa-Sabiiti, Jesca Olumide, Adesola O Osrin, David Powell-Jackson, Timothy Rasanathan, Kumanan Rasul, Imran Reid, Papaarangi Requejo, Jennifer Rohde, Sarah S Rollins, Nigel Romedenne, Magali Singh Sachdev, Harshpal Saleh, Rana Shawar, Yusra R Shiffman, Jeremy Simon, Jonathon Sly, Peter D Stenberg, Karin Tomlinson, Mark Ved, Rajani R Costello, Anthony The Helen Clark Foundation Auckland New Zealand Partnership for Maternal Newborn & Child Health Geneva Switzerland Senegal Presidency Dakar Senegal Department of Maternal Newborn Child and Adolescent Health WHO Geneva Switzerland Department of Health Systems Governance and Financing WHO Geneva Switzerland Health Division HIV Data and Analytics Division UNICEF New York NY United States HIV Data and Analytics Division UNICEF New York NY United States Department of International Health Johns Hopkins School of Public Health Baltimore MD United States School of Population Health University of Auckland Auckland New Zealand Department of Global Health and Development London School of Hygiene and Tropical Medicine London United Kingdom Health Systems Transformation Platform Tata Trusts Mumbai India Centre for Global Child Health Hospital for Sick Children Toronto OT Canada Global Financing Facility World Bank Washington DC United States Health Systems Research Unit South African Medical Research Council Cape Town South Africa Department of Health Management and Policy Beirut Lebanon Knowledge to Policy Center American University of Beirut Beirut Lebanon School of Public Health University of Western Cape Bellville South Africa Financing Alliance for Health Nairobi Kenya Institute for Global Health London United Kingdom Department of Economics London United Kingdom University College London London United Kingdom Health Policy and Systems Research Consultant Accra Ghana China Center for Health Development Studies Peking University Beijing China Program of Social Sciences and Health Latin American School of Social Sciences Buenos Aires Argentina Centre for Science and Environment New Delhi India Department of Maternal and Child Health Uganda Ministry of Health Kampala Uganda Institute of Child Health University of Ibadan Ibadan Nigeria Department of Health Systems WHO Phnom Penh Cambodia West and Central Regional Office UNICEF Dakar Senegal P

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Ontologizing Health Systems data at Scale: Making Translational Discovery a Reality

arXiv

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arXiv 2022年

作者： Callahan, Tiffany J. Stefanski, Adrianne L. Wyrwa, Jordan M. Zeng, Chenjie Ostropolets, Anna Banda, Juan M. Baumgartner, William A. Boyce, Richard D. Casiraghi, Elena Coleman, Ben D. Collins, Janine H. Deakyne-Davies, Sara J. Feinstein, James A. Haendel, Melissa A. Lin, Asiyah Y. Martin, Blake Matentzoglu, Nicolas A. Meeker, Daniella Reese, Justin Sinclair, Jessica Taneja, Sanya B. Trinkley, Katy E. Vasilevsky, Nicole A. Williams, Andrew Zhang, Xingman A. Denny, Joshua C. Robinson, Peter N. Ryan, Patrick Hripcsak, George Bennett, Tellen D. Hunter, Lawrence E. Kahn, Michael G. Computational Bioscience Program University of Colorado Anschutz Medical Campus AuroraCO80045 United States Department of Biomedical Informatics Columbia University Irving Medical Center New YorkNY10032 United States Department of Physical Medicine and Rehabilitation School of Medicine University of Colorado Anschutz Medical Campus AuroraCO80045 United States National Human Genome Research Institute National Institutes of Health BethesdaMD20892 United States Department of Computer Science Georgia State University AtlantaGA30303 United States Department of Biomedical Informatics University of Pittsburgh School of Medicine PittsburghPA15260 United States Computer Science Università degli Studi di Milano Milan20122 Italy Division of Environmental Genomics and Systems Biology Lawrence Berkeley National Laboratory BerkeleyCA94720 United States The Jackson Laboratory for Genomic Medicine FarmingtonCT06032 United States Department of Haematology University of Cambridge Cambridge United Kingdom Department of Research Informatics & Data Science Analytics Resource Center Children’s Hospital Colorado AuroraCO80045 United States University of Colorado Anschutz School of Medicine AuroraCO80045 United States Center for Health AI University of Colorado Anschutz Medical Campus AuroraCO80045 United States Departments of Biomedical Informatics and Pediatrics University of Colorado School of Medicine AuroraCO80045 United States Semanticly Athens Greece Yale School of Medicine New HavenCT06510 United States HealthLinc ValparaisoIN46383 United States Intelligent Systems Program University of Pittsburgh PittsburghPA15260 United States Department of Family Medicine University of Colorado Anschutz School of Medicine AuroraCO80045 United States Translational and Integrative Sciences Lab University of Colorado Anschutz Medical Campus AuroraCO80045 United States Tufts Institute for Clinical Research and Health Policy Studies Tufts University Bos

Background: Common data models solve many challenges of standardizing electronic health record (EHR) data, but are unable to semantically integrate all the resources needed for deep phenotyping. Open Biological and Biomedical Ontology (OBO) Foundry ontologies provide computable representations of biological knowledge and enable the integration of heterogeneous data. However, mapping EHR data to OBO ontologies requires significant manual curation and domain expertise. Objective: We introduce OMOP2OBO, an algorithm for mapping Observational Medical Outcomes Partnership (OMOP) vocabularies to OBO ontologies. Results: Using OMOP2OBO, we produced mappings for 92,367 conditions, 8611 drug ingredients, and 10,673 measurement results, which covered 68-99% of concepts used in clinical practice when examined across 24 hospitals. When used to phenotype rare disease patients, the mappings helped systematically identify undiagnosed patients who might benefit from genetic testing. Conclusions: By aligning OMOP vocabularies to OBO ontologies our algorithm presents new opportunities to advance EHR-based deep phenotyping. © 2022, CC BY.

关键词： Ontology

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