Development and prospective evaluation of a machine learning model to predict vomiting among pediatric cancer and hematopoietic cell transplant patients
We found that data in the EHR could be used to develop a retrospective ML model to predict vomiting among pediatric oncology and HCT inpatients. This model retained satisfactory performance in a prospective silent trial. Future plans will include deployment into clinical workflows and determining if the model improves vomiting control.
Authors
- Lin Guo (ORCID: https://orcid.org/0000-0002-5910-3464)
- Karim Jessa
- Adam P. Yan (ORCID: https://orcid.org/0000-0001-8300-3095)
- Emily Vettese
- Tal Schechter
- George Tomlinson (ORCID: https://orcid.org/0000-0002-9328-6399)
- L. Lee Dupuis (ORCID: https://orcid.org/0000-0002-7699-1061)
- Barry Cardiff (ORCID: https://orcid.org/0000-0003-1303-8115)
- Santiago Eduardo Arciniegas (ORCID: https://orcid.org/0009-0009-1750-5180)
- Priya Patel (ORCID: https://orcid.org/0000-0002-2872-5412)
- Lillian Sung (ORCID: https://orcid.org/0000-0003-0951-3091)
- Jiro Inoue
Institutions
- University of Toronto (CA)
- Hospital for Sick Children (CA)
- Toronto General Hospital (CA)
- SickKids Foundation (CA)
- Institute for Clinical Evaluative Sciences (CA)
Publication Details
- Journal
- BMC Cancer
- Published
- 2025-10-31
- DOI
- https://doi.org/10.1186/s12885-025-15137-1
- Primary Topic
- Nausea and vomiting management
- Type
- article
- Field-Weighted Citation Impact
- 0.00