Use of machine learning to identify patients at risk of sub-optimal adherence: study based on real-world data from 10,929 children using a connected auto-injector device

BACKGROUND: Our aim was to develop a machine learning model, using real-world data captured from a connected auto-injector device and from early indicators from the first 3 months of treatment, to predict sub-optimal adherence to recombinant human growth hormone (r-hGH) in patients with growth disor...

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Detalles Bibliográficos
Autores principales: Spataru, Amalia, van Dommelen, Paula, Arnaud, Lilian, Le Masne, Quentin, Quarteroni, Silvia, Koledova, Ekaterina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9261072/
https://www.ncbi.nlm.nih.gov/pubmed/35794586
http://dx.doi.org/10.1186/s12911-022-01918-2

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9261072/
https://www.ncbi.nlm.nih.gov/pubmed/35794586
http://dx.doi.org/10.1186/s12911-022-01918-2

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