Determining health care cost drivers in older Hodgkin lymphoma survivors using interpretable machine learning methods
As ML applications increase in predicting health care expenditure, researchers should consider implementing models in different phases of care to identify the changes in the predictors. Leading predictors of health care expenditures can be targeted for informed policy development to address financial hardship in HL survivors.
Authors
- Sabina O. Nduaguba (ORCID: https://orcid.org/0000-0001-6383-2603)
- Jay S. Patel (ORCID: https://orcid.org/0000-0003-0559-5958)
- Zasim Azhar Siddiqui (ORCID: https://orcid.org/0000-0002-6719-3228)
- Usha Sambamoorthi (ORCID: https://orcid.org/0000-0001-8311-1360)
- Yves Paul Vincent Mbous (ORCID: https://orcid.org/0000-0002-6642-1073)
- Virginia Scott
- Traci LeMasters (ORCID: https://orcid.org/0000-0002-6998-2650)
Institutions
- West Virginia University (US)
- University of North Texas (US)
- University of North Texas Health Science Center (US)
- Temple University (US)
Publication Details
- Journal
- Journal of Managed Care & Specialty Pharmacy
- Published
- 2025-03-28
- DOI
- https://doi.org/10.18553/jmcp.2025.31.4.406
- Primary Topic
- Economic and Financial Impacts of Cancer
- Type
- article
- Field-Weighted Citation Impact
- 0.00