Tammie Choi, Mahbub Sarkar, Maxine Bonham, Tina Brock, Ingrid Ann Brooks, Basia Diug, Dragan Ilic, Arunaz Kumar, Wee-Ming Lau, Jennifer Lindley, Julia Morphet, Margaret Simmons, Evelyn Volders, Paul J White, Caroline Wright, Claire Palermo
Using contribution analysis to evaluate health professions and health sciences programs
Frontiers in medicine
young breast cancer ybc patients often face poor prognosis hence necessary construct model accurately predict longterm survival early stage realize goal utilized data surveillance epidemiology end results seer databases january 2010 december 2020 meanwhile enrolled independent external cohort tianjin medical university cancer institute hospital study aimed develop validate prediction model constructed using random survival forest rsf machine learning algorithm applying least absolute shrinkage selection operator lasso regression analysis pinpointed key prognostic factors ybc patients used create prediction model capable forecasting 3year 5year 7year 10year survival rates ybc patients rsf model constructed study demonstrated exceptional performance achieving cindex values 0920 training set 0789 internal validation set 0701 external validation set outperforming cox regression model models calibration confirmed brier scores various time points showcasing excellent accuracy prediction decision curve analysis dca underscored models importance clinical application shapley additive explanations shap plots highlighted importance key variables rsf model also proved valuable risk stratification effectively categorized patients based survival risks summary study constructed wellperformed prediction model evaluation prognostic factors influencing longterm survival earlystage ybc patients significant risk stratification physicians handle ybc patients clinical settings