Table 2 Summary table of machine learning applied to MTX delayed elimination or Adverse reactions

Risk prediction for delayed clearance of high-dose methotrexate in pediatric hematological malignancies by machine learning [7]

Zhan M

(1) Hematocrit, risk classification, dose, SLC19A1 rs2838958, sex, dose

(2) SLC19A1 rs2838958, dose, sex

(1) C5.0 decision tree + SMOTE; (2) Nomogram

(1) AUROC = 0.807

(95% CI 0.724–0.889)

(2) AUROC = 0.690

(95% CI 0.594–0.787)

Predictive analysis of methotrexate elimination delay based on logistic regression model and ROC curve [13]

Wang Yang

SLCO1B1 T521C

Logistic

regression

AUROC = 0.751

(0.627–0.875)

Plasma creatinine as predictor of delayed elimination of high-dose methotrexate in childhood acute lymphoblastic leukemia: A Danish population-based study [15]

Schmidt, D

(1) Absolute increase in 36 h Plasma Creatinine

(2) Relative increasein 36 h Plasma Creatinine

(3) Infusion plasma MTX concentration

Linear

regression

(1) AUROC = 0.930

(95%CI 0.910-0-960)

(2) AUROC = 0.930

(95%CI 0.910-0-960)

(3) AUROC = 0.810

(95%CI 0.750-0-860)

Risk factors for high-dose methotrexate-induced nephrotoxicity [22]

Shinichiro Kawaguchi

Urine pH at day 1

Logistic regression

0.750

(95% CI 0.573–0.927)

Predicting Hepatotoxicity Associated with Low-Dose Methotrexate Using Machine Learning [37]

Hu, Qiaozhi

BMI, age, number of drugs and comorbidities, doses of folic acid, antibiotic use, gender, immunosuppressive agents, Glucocorticoid use, First MTX use, Drinking, Type 2 diabetes, Chinese traditional medicine, Dose of folic acid, Infectious liver disease, history of kidney disease

(1) XGBoost

(2) AdaBoost

(3) CatBoost

(4) GBDT

(5) LightGBM

(6) TPOT

(7) RF

(8) ANN

(1)0.94

(2)0.69

(3)0.91

(4)0.53

(5)0.87

(6)0.78

(7)0.97

(8)0.65