An Interoperable Machine Learning Pipeline for Pediatric Obesity Risk Estimation

Hamed Fayyaz, Mehak Gupta, Alejandra Perez Ramirez, Claudine Jurkovitz, H. Timothy Bunnell, Thao-Ly T. Phan, Rahmatollah Beheshti
Proceedings of the 4th Machine Learning for Health Symposium, PMLR 259:308-324, 2025.

Abstract

Reliable prediction of pediatric obesity can offer a valuable resource to providers, helping them engage in timely preventive interventions before the disease is established. Many efforts have been made to develop ML-based predictive models of obesity, and some studies have reported high predictive performances. However, no commonly used clinical decision support tool based on existing ML models currently exists. This study presents a novel end-to-end pipeline specifically designed for pediatric obesity prediction, which supports the entire process of data extraction, inference, and communication via an API or a user interface. While focusing only on routinely recorded data in pediatric electronic health records (EHRs), our pipeline uses a diverse expert-curated list of medical concepts to predict the 1-3 years risk of developing obesity. Furthermore, by using the Fast Healthcare Interoperability Resources (FHIR) standard in our design procedure, we specifically target facilitating low-effort integration of our pipeline with different EHR systems. In our experiments, we report the effectiveness of the predictive model as well as its alignment with the feedback from various stakeholders, including ML scientists, providers, health IT personnel, health administration representatives, and patient group representatives.

Cite this Paper

BibTeX
@InProceedings{pmlr-v259-fayyaz25a, title = {An Interoperable Machine Learning Pipeline for Pediatric Obesity Risk Estimation}, author = {Fayyaz, Hamed and Gupta, Mehak and Perez Ramirez, Alejandra and Jurkovitz, Claudine and Bunnell, H. Timothy and T. Phan, Thao-Ly and Beheshti, Rahmatollah}, booktitle = {Proceedings of the 4th Machine Learning for Health Symposium}, pages = {308--324}, year = {2025}, editor = {Hegselmann, Stefan and Zhou, Helen and Healey, Elizabeth and Chang, Trenton and Ellington, Caleb and Mhasawade, Vishwali and Tonekaboni, Sana and Argaw, Peniel and Zhang, Haoran}, volume = {259}, series = {Proceedings of Machine Learning Research}, month = {15--16 Dec}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v259/main/assets/fayyaz25a/fayyaz25a.pdf}, url = {https://proceedings.mlr.press/v259/fayyaz25a.html}, abstract = {Reliable prediction of pediatric obesity can offer a valuable resource to providers, helping them engage in timely preventive interventions before the disease is established. Many efforts have been made to develop ML-based predictive models of obesity, and some studies have reported high predictive performances. However, no commonly used clinical decision support tool based on existing ML models currently exists. This study presents a novel end-to-end pipeline specifically designed for pediatric obesity prediction, which supports the entire process of data extraction, inference, and communication via an API or a user interface. While focusing only on routinely recorded data in pediatric electronic health records (EHRs), our pipeline uses a diverse expert-curated list of medical concepts to predict the 1-3 years risk of developing obesity. Furthermore, by using the Fast Healthcare Interoperability Resources (FHIR) standard in our design procedure, we specifically target facilitating low-effort integration of our pipeline with different EHR systems. In our experiments, we report the effectiveness of the predictive model as well as its alignment with the feedback from various stakeholders, including ML scientists, providers, health IT personnel, health administration representatives, and patient group representatives.} }
Endnote
%0 Conference Paper %T An Interoperable Machine Learning Pipeline for Pediatric Obesity Risk Estimation %A Hamed Fayyaz %A Mehak Gupta %A Alejandra Perez Ramirez %A Claudine Jurkovitz %A H. Timothy Bunnell %A Thao-Ly T. Phan %A Rahmatollah Beheshti %B Proceedings of the 4th Machine Learning for Health Symposium %C Proceedings of Machine Learning Research %D 2025 %E Stefan Hegselmann %E Helen Zhou %E Elizabeth Healey %E Trenton Chang %E Caleb Ellington %E Vishwali Mhasawade %E Sana Tonekaboni %E Peniel Argaw %E Haoran Zhang %F pmlr-v259-fayyaz25a %I PMLR %P 308--324 %U https://proceedings.mlr.press/v259/fayyaz25a.html %V 259 %X Reliable prediction of pediatric obesity can offer a valuable resource to providers, helping them engage in timely preventive interventions before the disease is established. Many efforts have been made to develop ML-based predictive models of obesity, and some studies have reported high predictive performances. However, no commonly used clinical decision support tool based on existing ML models currently exists. This study presents a novel end-to-end pipeline specifically designed for pediatric obesity prediction, which supports the entire process of data extraction, inference, and communication via an API or a user interface. While focusing only on routinely recorded data in pediatric electronic health records (EHRs), our pipeline uses a diverse expert-curated list of medical concepts to predict the 1-3 years risk of developing obesity. Furthermore, by using the Fast Healthcare Interoperability Resources (FHIR) standard in our design procedure, we specifically target facilitating low-effort integration of our pipeline with different EHR systems. In our experiments, we report the effectiveness of the predictive model as well as its alignment with the feedback from various stakeholders, including ML scientists, providers, health IT personnel, health administration representatives, and patient group representatives.
APA
Fayyaz, H., Gupta, M., Perez Ramirez, A., Jurkovitz, C., Bunnell, H.T., T. Phan, T. & Beheshti, R.. (2025). An Interoperable Machine Learning Pipeline for Pediatric Obesity Risk Estimation. Proceedings of the 4th Machine Learning for Health Symposium, in Proceedings of Machine Learning Research 259:308-324 Available from https://proceedings.mlr.press/v259/fayyaz25a.html.

Related Material