We developed a generalizable machine learning framework, agnostic to specific cancer diagnosis, to improve the prediction of specific outcomes and to identify potentially-predictive features using oncology EHR-derived data. Customized models based on this framework could be applied to adverse event prediction, early detection of disease progression, and hospital readmission risk with relatively minimal labor duplication, streamlining HEOR opportunities.
Find the abstract for this project here.