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
van Dommelen, P.
Le Masne, Q.
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
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Recombinant human growth hormone
BMC Medical Informatics and Decision Making, 22 (22)