Analysis of real-world data on growth hormone therapy adherence using a connected injection device. - Ekaterina Koledova; Vincenzo Tornincasa; Paula van Dommelen (BMC Medical Informatics Decis. Mak., 2020)

A Data-Driven Intervention Framework for Improving Adherence to Growth Hormone Therapy Based on Clustering Analysis and Traffic Light Alerting Systems. - Matheus Araújo 001; Paula van Dommelen; Jaideep Srivastava; Ekaterina Koledova (EFMI-STC, 2021)

Using Deep Learning for Individual-Level Predictions of Adherence with Growth Hormone Therapy. - Matheus Araújo 001; Paula van Dommelen; Ekaterina Koledova; Jaideep Srivastava (MIE, 2021)

High Engagement of Patients Monitored by a Digital Health Ecosystem Indicates Significant Improvements of Key r-hGH Treatment Metrics. - Amalia Spataru; Silvia Quarteroni; Lilian Arnaud; Paula van Dommelen; Ekaterina Koledova; Quentin Le Masne (MIE, 2021)

Observed High Adherence to Recombinant Human Growth Hormone Treatment Using a Multi-Component Approach to Improve Adherence in Individuals with Growth Disorders. - Kate Morgan; Paula van Dommelen; Ana-Rita Batista; Ekaterina Koledova (MIE, 2022)

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. - Amalia Spataru; Paula van Dommelen; Lilian Arnaud; Quentin Le Masne; Silvia Quarteroni; Ekaterina Koledova (BMC Medical Informatics Decis. Mak., 2022)

Preferences for Injection Device Settings and the Association with Adherence to Growth Hormone Treatment in Patient with Growth Disorders. - Paula van Dommelen; Octavio Rivera Romero; Lilian Arnaud; Chantal Zucchiatti; Ekaterina Koledova (MIE, 2024)