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

被引:0
作者
Amalia Spataru
Paula van Dommelen
Lilian Arnaud
Quentin Le Masne
Silvia Quarteroni
Ekaterina Koledova
机构
[1] ETH Zürich and EPFL,Swiss Data Science Center
[2] The Netherlands Organization for Applied Scientific Research TNO,Connected Health and Devices, Global Healthcare Operations, Ares Trading S.A.
[3] An Affiliate of Merck KGaA,Global Medical Affairs Cardiometabolic & Endocrinology
[4] Merck Healthcare KGaA,undefined
来源
BMC Medical Informatics and Decision Making | / 22卷
关键词
Adherence; Auto-injector; Connected device; Digital health; Indicator; Machine learning; Recombinant human growth hormone;
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