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Journal Articles
- 2022
[j2]Amalia Spataru, Paula van Dommelen
, Lilian Arnaud, Quentin Le Masne, Silvia Quarteroni, Ekaterina Koledova:
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. BMC Medical Informatics Decis. Mak. 22(1): 179 (2022)- 2020
- [j1]Ekaterina Koledova, Vincenzo Tornincasa, Paula van Dommelen:
Analysis of real-world data on growth hormone therapy adherence using a connected injection device. BMC Medical Informatics Decis. Mak. 20(1): 176 (2020)
Conference and Workshop Papers
- 2024
- [c5]Paula van Dommelen, Octavio Rivera Romero, Lilian Arnaud, Chantal Zucchiatti, Ekaterina Koledova:
Preferences for Injection Device Settings and the Association with Adherence to Growth Hormone Treatment in Patient with Growth Disorders. MIE 2024: 38-42 - 2022
- [c4]Kate Morgan, Paula van Dommelen, Ana-Rita Batista, Ekaterina Koledova:
Observed High Adherence to Recombinant Human Growth Hormone Treatment Using a Multi-Component Approach to Improve Adherence in Individuals with Growth Disorders. MIE 2022: 817-818 - 2021
- [c3]Matheus Araújo, Paula van Dommelen, Jaideep Srivastava, Ekaterina Koledova:
A Data-Driven Intervention Framework for Improving Adherence to Growth Hormone Therapy Based on Clustering Analysis and Traffic Light Alerting Systems. EFMI-STC 2021: 23-27 - [c2]Matheus Araújo, Paula van Dommelen, Ekaterina Koledova, Jaideep Srivastava:
Using Deep Learning for Individual-Level Predictions of Adherence with Growth Hormone Therapy. MIE 2021: 133-137 - [c1]Amalia Spataru, Silvia Quarteroni, Lilian Arnaud, Paula van Dommelen, Ekaterina Koledova, Quentin Le Masne:
High Engagement of Patients Monitored by a Digital Health Ecosystem Indicates Significant Improvements of Key r-hGH Treatment Metrics. MIE 2021: 829-833
Coauthor Index
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