Table 1.
Variables included in models.
Table 2.
Characteristics of study samples.
Fig 1.
Test AUC by dataset type by algorithm.
Addition of historical information improves predictive performance significantly compared to using triage information alone. Patient history alone can predict admission to a reasonable degree.
Table 3.
Summary of statistical measures for each model.
Fig 2.
Model performance on increasing fractions of the training set.
95% CIs are shown in gray bars. All three algorithms reach maximum performance at 50% of the training set or less. LR reaches maximum performance earlier than XGBoost or DNN.
Fig 3.
Variables from the full XGBoost model ordered by information gain.
Row names represent the variables in the design matrix post one-hot encoding (see S1 Table for name descriptions). Points represent the mean information gain from a hundred runs of XGBoost. Horizontal lines show bootstrapped 95% confidence intervals.