Title
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
Author
Spataru, A.
van Dommelen, P.
Arnaud, L.
Le Masne, Q.
Quarteroni, S.
Koledova, E.B.
Publication year
2022
Abstract
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 disorders. Methods. Adherence to r-hGH treatment was assessed in children (aged
Subject
Adherence
Auto-injector
Connected device
Digital health
Indicator
Machine learning
Recombinant human growth hormone
To reference this document use:
http://resolver.tudelft.nl/uuid:0875fc64-81a5-48e0-aee1-b452dc1e4e26
DOI
https://doi.org/10.1186/s12911-022-01918-2
TNO identifier
972458
Source
BMC Medical Informatics and Decision Making, 22 (22)
Document type
article