Clinical deployment environments: Five pillars of translational machine learning for health

Machine Learning for Health (ML4H) has demonstrated efficacy in computer imaging and other self-contained digital workflows, but has failed to substantially impact routine clinical care. This is no longer because of poor adoption of Electronic Health Records Systems (EHRS), but because ML4H needs an...

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Detalles Bibliográficos
Autores principales: Harris, Steve, Bonnici , Tim, Keen, Thomas, Lilaonitkul, Watjana, White, Mark J., Swanepoel, Nel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Digital Health
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9437594/
https://www.ncbi.nlm.nih.gov/pubmed/36060542
http://dx.doi.org/10.3389/fdgth.2022.939292

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9437594/
https://www.ncbi.nlm.nih.gov/pubmed/36060542
http://dx.doi.org/10.3389/fdgth.2022.939292

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