Table 1.
Programming languages and modeling approaches used by each participant for the Harmonized Pediatric Trauma Brain Injury Data Challenge.
| Participant | Language | FSS | Mortality |
|---|---|---|---|
| P01 | R | Random Forest | Random Forest |
| P03 | R | Gradient Boost | Gradient Boost |
| P07 | R | Linear model | Logistic Regression |
| P11 | python | Random Forest | Support Vector Machine |
| P12 | R | Linear model | Logistic Regression |
| P14 | R | Random Forest | Random Forest |
| P15 | python | Ridge Regression | Random Forest |
| P20 | R | Linear model | Logistic Regression |
| P22 | R | Stacked Models | Stacked Models |
| P24 | python | Random Forest | Random Forest |
| P26 | R | Random Forest | Random Forest |
The three linear models used for FSS modeling used a Gaussian Response.