“…In particular, the ML models identified the following factors as predictive for CR: de novo AML, extramedullary AML, double-mutated CEBPA, mutations of CEBPA-bZIP, NPM1, FLT3-ITD, ASXL1, RUNX1, SF3B1, IKZF1, TP53, U2AF1, t(8;21), inv (16)/t(16;16), del (5)/del(5q), del (17)/del(17p), normal or complex karyotypes, age and hemoglobin. 17 The same ML models autonomously selected the following factors as predictive for 2-year OS: t(8;21), del (5)/del(5q), inv(16)/t (16;16), del(17)/del(17p), double-mutated CEBPA, CEBPA-bZIP, NPM1, FLT3-ITD, DNMT3A, SF3B1, U2AF1, TP53, age, white blood cell count, peripheral blast count, serum LDH, hemoglobin and extramedullary involvement. 17 Several studies have shown the superiority of artificial intelligence as compared to the ELN 2017 score in predicting survival or personalized outcomes.…”