Development of Machine Learning Algorithms for the Prediction of Financial Toxicity in Localized Breast Cancer Following Surgical Treatment
March 26, 2021 | Anaeze C. Offodile II, Chris Sidey-Gibbons, André Pfob, Malke Assad, Stefanos Boukovalas, Yu-Li Lin, Jesse Creed Selber, Charles Butler
Table of Contents
Author(s)
Anaeze C. Offodile II
Former Nonresident ScholarChris Sidey-Gibbons
André Pfob
Malke Assad
Stefanos Boukovalas
Yu-Li Lin
Jesse Creed Selber
Charles Butler
Abstract
Financial burden caused by cancer treatment is associated with material loss, distress, and poorer outcomes. Financial resources exist to support patients but identification of need is difficult. The authors sought to develop and test a tool to accurately predict an individual's risk of financial toxicity based on clinical, demographic, and patient-reported data prior to initiation of breast cancer treatment.
Access the full journal article in JCO Clinical Cancer Informatics.