Cox proportional hazards deep neural network identifies peripheral blood complete remission to be at least equivalent to morphologic complete remission in predicting outcomes of patients treated with azacitidine-A prospective cohort study by the AGMT
L Pleyer* (First author), M Vaisband, M Drost, M Pfeilstöcker, R Stauder, S Heibl, H Sill, M Girschikofsky, M Stampfl-Mattersberger, A Pichler, B Hartmann, A Petzer, M Schreder, CA Schmitt, S Vallet, T Melchardt (Co-author), A Zebisch, P Pichler, N Zaborsky (Co-author), S Machherndl-SpandlD Wolf, F Keil, J Hasenauer, J Larcher-Senn, R Greil (Last author)
Research output: Contribution to journal › Original Article (Journal) › peer-review
1Citation
(Web of Science)
Fingerprint
Dive into the research topics of 'Cox proportional hazards deep neural network identifies peripheral blood complete remission to be at least equivalent to morphologic complete remission in predicting outcomes of patients treated with azacitidine-A prospective cohort study by the AGMT'. Together they form a unique fingerprint.