Table 2 Area Under the Curve (AUC) of models generated with Python, where the Word2Vec features are the sum of the numeric vectors of the last 25 codes
From: Effective hospital readmission prediction models using machine-learned features
Validation and Test AUCs of Different Models and Feature Configurations | |||
|---|---|---|---|
Model | Features | Average Cross-Validation AUC (St. Dev.) | Test AUC |
Logistic Regression (LR) | Manual | 0.7612 (0.004123) | 0.747 |
Word2Vec | 0.7470 (0.005600) | 0.757 | |
Manual and Word2Vec | 0.7862 (0.005758) | 0.783 | |
Gradient Boosting Machine (GBM) | Manual | 0.8037 (0.004001) | 0.804 |
Word2Vec | 0.7700 (0.005138) | 0.768 | |
Manual and Word2Vec | 0.8138 (0.004534) | 0.813 | |
Manual and Word2Vec†| 0.8249 (0.004549) | 0.826 | |
Logistic Regression (LR) | LACE | 0.6548 (0.006444) | 0.655 |