Fig 1.
Process flow diagram of the process of building a predictive trauma model.
The dataset is acquired from the National Trauma Data Bank (NTDB) and any patient with more than 2 missing data fields were removed from the dataset (cleaning). The data consisted of relational tables with each patient identified by a unique incident key. By merging using the incident key, it was possible to generate a matrix of data where each row represented a unique patient and each column represented a unique feature. Features were included in the column based on physiological information that was expected to contribute to the outcome of the model. This included age, gender, vital signs, coma and severity scores, and comorbidities. Facility and demographic information (other than age) was not included in the analysis. The dataset was then divided into a balanced training set (equal number of survived and deceased patients) and a test set, a model was trained on the training set with optimized hyperparameters (see S2 Fig), and then the results reported and analyzed.
Fig 2.
The receiver operating characteristic curves (ROC) for 2 different cases.
The true positive rate (TPR) is plotted on the y-axis and the false positive rate (FPR) is plotted on the x-axis for classification thresholds between 0 and 1. In the red curve, only 8 easily measurable vital signs or scores were included in the prediction while the black curve included these and the comorbidities. A full list of features in each case can be found in S1 Table. All results are reported using the second case because the required inputs can be measured rapidly, while knowledge of the comorbidities of a patient is less likely. The heat map in the insert plots the 8 feature values of 100 randomly selected patients, illustrating the high dimensionality of the problem. While no obvious pattern can be seen by humans in the heat map, the algorithm is able to find and quantify one. 4 zoomed-in examples are provided for clarity. Note that each column is normalized by its own feature value range.
Fig 3.
Partial dependence curves showing how the prediction of the model is globally influenced by each of the features.
Pulse rate and systolic blood pressure display threshold behavior, where the probability of survival can decrease at HR > 100 beats / min and SBP < 110mmHg [30].
Fig 4.
Histograms of the survival probabilities for survived and deceased patients.
If probabilities of death greater than 20% are marked as high risk, then ~96% of the deceased patients would been labeled.
Fig 5.
SHAP feature importance metrics for 4 patients that were correctly predicted as survived or deceased.
Output values (bold), expressed as log odds ratio of probability of survival to probability of deceased (i.e. log()), that are < 0 represent deceased patients (Cases A, C, D). Blue bars indicate that the feature value is increasing the probability of survival while red bars indicate that the feature is decreasing it.
Fig 6.
SHAP feature importance metrics for 4 patients that were incorrectly predicted as deceased.
Output values (bold), expressed as log odds ratio of probability of survival to probability of deceased (i.e. log()), that are < 0 represent deceased patients. Blue bars indicate that the feature value is increasing the probability of survival while red bars indicate that the feature is decreasing it. In all 4 cases, there was one feature that dominated the model prediction.
Fig 7.
SHAP feature importance metrics for 4 patients that were incorrectly predicted as survived.
Similar to incorrectly predicting deceased cases, there was one feature that dominated the model prediction.