Using the National Trauma Data Bank (NTDB) and machine learning to predict trauma patient mortality at admission.

ABSTRACT: A 400-estimator gradient boosting classifier was trained to predict survival probabilities of trauma patients. The National Trauma Data Bank (NTDB) provided 799233 complete patient records (778303 survivors and 20930 deaths) each containing 32 features, a number further reduced to only 8 features via the permutation importance method. Importantly, the 8 features can all be readily determined at admission: systolic blood pressure, heart rate, respiratory rate, temperature, oxygen saturation, gender, age and Glasgow coma score. Since death was rare, a rebalanced training set was used to train the model. The model is able to predict a survival probability for any trauma patient and accurately distinguish between a deceased and survived patient in 92.4% of all cases. Partial dependence curves

SUBMITTER: Tsiklidis EJ 

PROVIDER: S-EPMC7671512 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature