Deduction learning for precise noninvasive measurements of blood glucose with a dozen rounds of data for model training

Personalized modeling has long been anticipated to approach precise noninvasive blood glucose measurements, but challenged by limited data for training personal model and its unavoidable outlier predictions. To overcome these long-standing problems, we largely enhanced the training efficiency with t...

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Detalles Bibliográficos
Autores principales: Lu, Wei-Ru, Yang, Wen-Tse, Chu, Justin, Hsieh, Tung-Han, Yang, Fu-Liang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9021306/
https://www.ncbi.nlm.nih.gov/pubmed/35444228
http://dx.doi.org/10.1038/s41598-022-10360-3

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9021306/
https://www.ncbi.nlm.nih.gov/pubmed/35444228
http://dx.doi.org/10.1038/s41598-022-10360-3

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