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Table 10 Selected variables for each scenario, considering varying time periods and hospitals. Variables that appear in both the combined scenario and either of the individual hospital scenarios within the same time period are highlighted in bold

From: Identification of relevant features using SEQENS to improve supervised machine learning models predicting AML treatment outcome

Time

H12O+LAFE

H12O

LAFE

90 days

Age

Age

Age

TP53_VAF

TP53_VAF

ASXL1_VAF

SRSF2_VAF

SRSF2_VAF

SRSF2_VAF

−7/7q

−7/7q

−7/7q

IDH1_VAF

IDH1_VAF

−17/17p

JAK2_VAF

JAK2_VAF

NPM1_VAF

−5/5q

EPOR_VAF

−5/5q

EZH2_VAF

  

FLT3_VAF

  

180 days

Age

Age

Age

TP53_VAF

TP53_VAF

TP53_VAF

NPM1_VAF

FLT3_ITK

NPM1_VAF

−7/7q

−7/7q

−7/7q

ASXL1_VAF

JAK2_VAF

ASXL1_VAF

EZH2_VAF

EZH2_VAF

−5/5q

IDH1_VAF

KIT_VAF

IDH1_VAF

SRSF2_VAF

SRSF2_VAF

wbc

TET2_VAF

 

TET2_VAF

EPOR_VAF

EPOR_VAF

 

U2AF1_VAF

U2AF1_VAF

 

FLT3_ITD

  

1 year

Age

Age

Age

−7/7q

−7/7q

−7/7q

EZH2_VAF

EZH2_VAF

−17/17p

TP53_VAF

FLT3_ITK

TP53_VAF

BM_Blasts

Gender

BM_Blasts

NPM1_VAF

KMT2A_VAF

NPM1_VAF

KIT_VAF

KIT_VAF

ASXL1_VAF

TET2_VAF

ETV6_VAF

TET2_VAF

  

Gender