Cargando…

Who was at risk for COVID-19 late in the US pandemic? Insights from a population health machine learning model

Notable discrepancies in vulnerability to COVID-19 infection have been identified between specific population groups and regions in the USA. The purpose of this study was to estimate the likelihood of COVID-19 infection using a machine-learning algorithm that can be updated continuously based on hea...

Descripción completa

Detalles Bibliográficos
Autores principales:	Adeoye, Elijah A., Rozenfeld, Yelena, Beam, Jennifer, Boudreau, Karen, Cox, Emily J., Scanlan, James M.
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Springer Berlin Heidelberg 2022
Materias:	Original Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9090454/ https://www.ncbi.nlm.nih.gov/pubmed/35538201 http://dx.doi.org/10.1007/s11517-022-02549-5

Ejemplares similares

A model of disparities: risk factors associated with COVID-19 infection
por: Rozenfeld, Yelena, et al.
Publicado: (2020)

Significant impacts of the COVID-19 pandemic on race/ethnic differences in US mortality
por: Aburto, José Manuel, et al.
Publicado: (2022)

Gene-Environment Covariance as a Mechanism of Declines in Openness in Late Adulthood
por: Beam, Christopher, et al.
Publicado: (2020)

Early Stage Machine Learning–Based Prediction of US County Vulnerability to the COVID-19 Pandemic: Machine Learning Approach
por: Mehta, Mihir, et al.
Publicado: (2020)

Second opinion needed: communicating uncertainty in medical machine learning
por: Kompa, Benjamin, et al.
Publicado: (2021)

Detection of Osteoporosis from Percussion Responses Using an Electronic Stethoscope and Machine Learning
por: Scanlan, Jamie, et al.
Publicado: (2018)

Odonata: Who They Are and What They Have Done for Us Lately: Classification and Ecosystem Services of Dragonflies
por: May, Michael L.
Publicado: (2019)

Machine learning in aquaculture: hunger classification of lates calcarifer
por: Mohd Razman, Mohd Azraai, et al.
Publicado: (2020)

Machine Learning Prediction of Treatment Outcome in Late-Life Depression
por: Grzenda, Adrienne, et al.
Publicado: (2021)

COVID-19 and Gastrointestinal Tract Symptoms: Recognition, Containment, and Learning From the Past
por: McDermott, Carmen V., et al.
Publicado: (2020)

Pandemic velocity: Forecasting COVID-19 in the US with a machine learning & Bayesian time series compartmental model
por: Watson, Gregory L., et al.
Publicado: (2021)

Machine learning shows that the Covid-19 pandemic is impacting U.S. public companies unequally by changing risk structures
por: Cao, Likun, et al.
Publicado: (2022)

Advancing medical affair capabilities and insight generation through machine learning techniques
por: Ng, Karen Ka Yan, et al.
Publicado: (2023)

Responsible Machine Learning
por: Hall, Patrick, et al.
Publicado: (2020)

Study becomes insight: Ecological learning from machine learning
por: Yu, Qiuyan, et al.
Publicado: (2021)

Segmentation analysis of the unvaccinated US adult population 2 years into the COVID-19 pandemic, 1 December 2021 to 7 February 2022
por: Agaku, Israel, et al.
Publicado: (2023)

Nothing about us without us: involving patient collaborators for machine learning applications in rheumatology
por: Shoop-Worrall, Stephanie J W, et al.
Publicado: (2021)

Trends and Focus of Machine Learning Applications for Health Research
por: Beaulieu-Jones, Brett, et al.
Publicado: (2019)

The Use of Machine Learning to Predict Late Arrivals at the Adult Outpatient Department
por: Aldhoayan, Mohammed D, et al.
Publicado: (2023)

Predicting Psychological Distress Amid the COVID-19 Pandemic by Machine Learning: Discrimination and Coping Mechanisms of Korean Immigrants in the U.S.
por: Choi, Shinwoo, et al.
Publicado: (2020)

Whether the weather will help us weather the COVID-19 pandemic: Using machine learning to measure twitter users’ perceptions
por: Gupta, Marichi, et al.
Publicado: (2021)

Machine learning for patient risk stratification: standing on, or looking over, the shoulders of clinicians?
por: Beaulieu-Jones, Brett K., et al.
Publicado: (2021)

A framework for seroepidemiologic investigations in future pandemics: insights from an evaluation of WHO’s Unity Studies initiative
por: Hennessey, Karen, et al.
Publicado: (2023)

Seroprevalence of Epstein-Barr Virus Infection in U.S. Children Ages 6-19, 2003-2010
por: Dowd, Jennifer Beam, et al.
Publicado: (2013)

Application of machine learning in understanding atherosclerosis: Emerging insights
por: Munger, Eric, et al.
Publicado: (2021)

An Insight into the Machine-Learning-Based Fileless Malware Detection
por: Khalid, Osama, et al.
Publicado: (2023)

Effect of COVID19 pandemic on Argentinian Fabry and Gaucher patients
por: Rozenfeld, Paula A., et al.
Publicado: (2022)

The Impact of Bisphenol A and Triclosan on Immune Parameters in the U.S. Population, NHANES 2003–2006
por: Rees Clayton, Erin M., et al.
Publicado: (2011)

Applying machine learning techniques to the identification of late-onset hypogonadism in elderly men
por: Lu, Ti, et al.
Publicado: (2016)

A Machine Learning Approach to Monitor the Emergence of Late Intrauterine Growth Restriction
por: Pini, Nicolò, et al.
Publicado: (2021)

Construction of machine learning-based models for cancer outcomes in low and lower-middle income countries: A scoping review
por: Adeoye, John, et al.
Publicado: (2022)

Natural Language Processing and Machine Learning for Detection of Respiratory Illness by Chest CT Imaging and Tracking of COVID-19 Pandemic in the US
por: Cury, Ricardo C., et al.
Publicado: (2021)

Different Coping Patterns among US Graduate and Undergraduate Students during COVID-19 Pandemic: A Machine Learning Approach
por: Zhao, Yijun, et al.
Publicado: (2022)

Python machine learning: unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics
por: Raschka, Sebastian, et al.
Publicado: (2015)

Mastering .NET machine learning: master the art of machine learning with .NET and gain insight into real-world applications
por: Dixon, Jamie
Publicado: (2016)

Experiences of Women Who Gave Birth in US Hospitals During the COVID-19 Pandemic
por: Mollard, Elizabeth, et al.
Publicado: (2021)

Let Us Not Forget the Victims of COVID-19 Pandemics Who Did Not Die With the Coronavirus
por: Pashkevich, Anton, et al.
Publicado: (2022)

Artificial Intelligence Based on Machine Learning in Pharmacovigilance: A Scoping Review
por: Kompa, Benjamin, et al.
Publicado: (2022)

Who develops pandemic fatigue? Insights from Latent Class Analysis
por: Taylor, Steven, et al.
Publicado: (2022)

US primary care in 2029: A Delphi survey on the impact of machine learning
por: Blease, Charlotte, et al.
Publicado: (2020)

Cannot write session to /tmp/vufind_sessions/sess_funfrvhgdadasil5olqdep8h6s