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Table 1 Ten most important variables for model to predict outcomes

From: Machine learning to predict hemorrhage and thrombosis during extracorporeal membrane oxygenation

Random forest model* Chi-square model
 Heparin drip rate—maximum dosage Heparin drip rate—maximum dosage
 Heparin drip rate—mean dosage Heparin drip rate—mean dosage
 PTT—lowest value Heparin drip rate—minimum dosage
 Activated clotting time—highest value PTT—highest value
 Platelet count—highest value PTT—mean value
 Race PTT—lowest value
 ECMO configuration INR—highest value
 ECMO—double-lumen cannulation INR—mean value
 Drainage cannula size INR—lowest value
 Drainage cannula site Activated clotting time—highest value
 ECMO—double-lumen cannulation Heparin drip rate—maximum dosage
 Platelet—lowest value Heparin drip rate—mean dosage
 Transfusion of cryoglobulin Heparin drip rate—minimum dosage
 Transfusion of platelets PTT—highest value
 Body mass index PTT—mean value
 Renal replacement therapy PTT—lowest value
 ECMO—duration INR—highest value
 ECMO indication—status asthmaticus INR—mean value
 ECMO indication—PH/right ventricular failure INR—lowest value
 Platelet count—mean value Activated clotting time—highest value
  1. ECMO extracorporeal membrane oxygenation, PH pulmonary hypertension, PTT partial thromboplastin time, INR international normalized ratio
  2. *p > 0.05, none of the individual features significantly contributed to the model’s performance