ArticleBMC medical informatics and decision making2022
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.
Article in BMC medical informatics and decision making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The trial behind it
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Who cites it
7 citing papers in PubMed, 16 citations in OpenAlex.
- Acceptability and feasibility of digital gamified cognitive training intervention for preschool children with cancer-related cognitive late effects: a mixed-methods study.BMC psychology · 2026Article
- Relational Attributes Associated with Treatment Adherence: A Scoping Review.Patient preference and adherence · 2026Review
- Risks and benefits associated with the primary functions of artificial intelligence powered autoinjectors.Frontiers in medical technology · 2024Article
- Use of connected injection device has a positive effect on catch-up growth in patients with growth disorders treated with growth hormone therapy.Frontiers in endocrinology · 2024Article
- Opportunities for digitally-enabled personalization and decision support for pediatric growth hormone therapy.Frontiers in endocrinology · 2024Review
- Precision diagnostics in children.Cambridge prisms. Precision medicine · 2023Review
- Curve matching to predict growth in patients receiving growth hormone therapy: An interpretable & explainable method.Frontiers in endocrinology · 2022Observational
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOur 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.
methodsAdherence to r-hGH treatment was assessed in children (aged < 18 years) who started using a connected auto-injector device (easypod™), and transmitted injection data for ≥ 12 months. Adherence in the following 3, 6, or 9 months after treatment start was categorized as optimal (≥ 85%) versus sub-optimal (< 85%). Logistic regression and tree-based models were applied.
resultsData from 10,929 children showed that a random forest model with mean and standard deviation of adherence over the first 3 months, infrequent transmission of data, not changing certain comfort settings, and starting treatment at an older age was important in predicting the risk of sub-optimal adherence in the following 3, 6, or 9 months. Sensitivities ranged between 0.72 and 0.77, and specificities between 0.80 and 0.81.
conclusionsTo the authors' knowledge, this is the first attempt to integrate a machine learning model into a digital health ecosystem to help healthcare providers to identify patients at risk of sub-optimal adherence to r-hGH in the following 3, 6, or 9 months. This information, together with patient-specific indicators of sub-optimal adherence, can be used to provide support to at-risk patients and their caregivers to achieve optimal adherence and, subsequently, improve clinical outcomes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.