Cargando…

Who does the model learn from?

Detalles Bibliográficos
Autores principales:	Charpignon, Marie-Laure, Celi, Leo Anthony, Samuel, Mathew Cherian
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	2021
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9148181/ https://www.ncbi.nlm.nih.gov/pubmed/33858816 http://dx.doi.org/10.1016/S2589-7500(21)00057-1

Ejemplares similares

Unsupervised learning for county-level typological classification for COVID-19 research
por: Lai, Yuan, et al.
Publicado: (2020)

Village mentoring and hive learning: The MIT Critical Data experience
por: Cosgriff, Christopher V., et al.
Publicado: (2021)

Sources of bias in artificial intelligence that perpetuate healthcare disparities—A global review
por: Celi, Leo Anthony, et al.
Publicado: (2022)

Deep learning to predict long-term mortality in patients requiring 7 days of mechanical ventilation
por: George, Naomi, et al.
Publicado: (2021)

A Method to Explore Variations of Ventilator-Associated Event Surveillance Definitions in Large Critical Care Databases in the United States
por: Wong, An-Kwok Ian, et al.
Publicado: (2022)

Patient diversity and author representation in clinical studies supporting the Surviving Sepsis Campaign guidelines for management of sepsis and septic shock 2021: a systematic review of citations
por: Nazer, Lama, et al.
Publicado: (2023)

Ensuring machine learning for healthcare works for all
por: McCoy, Liam G, et al.
Publicado: (2020)

PLOS Digital Health, a new journal driving transformation in the delivery of equitable and unbiased healthcare
por: Celi, Leo Anthony
Publicado: (2022)

Recalibration of deep learning models for abnormality detection in smartphone-captured chest radiograph
por: Kuo, Po-Chih, et al.
Publicado: (2021)

The myth of generalisability in clinical research and machine learning in health care
por: Futoma, Joseph, et al.
Publicado: (2020)

Equity in essence: a call for operationalising fairness in machine learning for healthcare
por: Wawira Gichoya, Judy, et al.
Publicado: (2021)

Scalable data systems require creating a culture of continuous learning
por: Brogan, James, et al.
Publicado: (2021)

Machine Learning for Pulmonary and Critical Care Medicine: A Narrative Review
por: Mlodzinski, Eric, et al.
Publicado: (2020)

Big data in healthcare: are we close to it?
por: Naidus, Elliot, et al.
Publicado: (2016)

Exploiting temporal relationships in the prediction of mortality
por: Cosgriff, Christopher V, et al.
Publicado: (2020)

Reinforcement Learning for Clinical Decision Support in Critical Care: Comprehensive Review
por: Liu, Siqi, et al.
Publicado: (2020)

Impact of non-pharmaceutical interventions, weather, vaccination, and variants on COVID-19 transmission across departments in France
por: Paireau, Juliette, et al.
Publicado: (2023)

Who does what? Comments from radiographers
por: Cush, SJ
Publicado: (2000)

From quality improvement to equality improvement projects: A scoping review and framework
por: Gallifant, Jack, et al.
Publicado: (2023)

Potential Adverse Effects of Broad-Spectrum Antimicrobial Exposure in the Intensive Care Unit
por: Wiens, Jenna, et al.
Publicado: (2017)

Evaluating criminal justice reform during COVID-19: The need for a novel sentiment analysis package
por: Ramjee, Divya, et al.
Publicado: (2022)

Counterintuitive results from observational data: a case study and discussion
por: Doty, Erik, et al.
Publicado: (2019)

The “inconvenient truth” about AI in healthcare
por: Panch, Trishan, et al.
Publicado: (2019)

Author Correction: The reproducibility crisis in the age of digital medicine
por: Stupple, Aaron, et al.
Publicado: (2019)

The reproducibility crisis in the age of digital medicine
por: Stupple, Aaron, et al.
Publicado: (2019)

Urban Intelligence for Pandemic Response: Viewpoint
por: Lai, Yuan, et al.
Publicado: (2020)

How to organise a datathon for bridging between data science and healthcare? Insights from the Technion-Rambam machine learning in healthcare datathon event
por: Sobel, Jonathan, et al.
Publicado: (2023)

The PLOS ONE collection on machine learning in health and biomedicine: Towards open code and open data
por: Celi, Leo A., et al.
Publicado: (2019)

World who is who and does what in environment & conservation /
Publicado: (1997)

Gamblers seeking treatment: Who does and who doesn’t?
por: BRAUN, BARBARA, et al.
Publicado: (2014)

Big data in global health: improving health in low- and middle-income countries
por: Wyber, Rosemary, et al.
Publicado: (2015)

Racial Disparities in Invasive ICU Treatments Among Septic Patients: High Resolution Electronic Health Records Analysis from MIMIC-IV
por: Mohammed, Sara, et al.
Publicado: (2023)

Improving community health-care screenings with smartphone-based AI technologies
por: Mantena, Sreekar, et al.
Publicado: (2021)

Data sharing in the era of COVID-19
por: Cosgriff, Christopher V, et al.
Publicado: (2020)

Circadian rhythm in critically ill patients: Insights from the eICU Database
por: Beyer, Sebastian E., et al.
Publicado: (2021)

State of the art review: the data revolution in critical care
por: Ghassemi, Marzyeh, et al.
Publicado: (2015)

COVID-19: Putting the General Data Protection Regulation to the Test
por: McLennan, Stuart, et al.
Publicado: (2020)

A systematic review of federated learning applications for biomedical data
por: Crowson, Matthew G., et al.
Publicado: (2022)

Risk of mortality and cardiopulmonary arrest in critical patients presenting to the emergency department using machine learning and natural language processing
por: Fernandes, Marta, et al.
Publicado: (2020)

Deep learning in veterinary medicine, an approach based on CNN to detect pulmonary abnormalities from lateral thoracic radiographs in cats
por: Dumortier, Léo, et al.
Publicado: (2022)

Cannot write session to /tmp/vufind_sessions/sess_mhua63vjo1q69ji87qbsnbugs7