Global inequities in access to cancer diagnostics and treatment contribute to wide variation in cancer mortality-to-incidence ratios (MIRs), a proxy for survival. We aimed to develop an interpretable machine learning framework to quantify country-specific health system contributors to MIR and inform policy prioritization.
Machine learning reveals country-specific drivers of global cancer outcomes
Annals of Oncology | | M.S. Patel, C.S. Pramesh, N.N. Sanford, E.J.G. Feliciano, P.L. Nguyen, P. Iyengar, T.P. Kingham, J. Willmann, B.A. Mahal, N.Y. Lee, M.J.K. Magsanoc-Alikpala, M. Mutebi, J.F. Wu, J.P.G. Robredo, E.C. Dee
Topics: oncology