Table 7.
A comparison of our final University of Washington Medicine model and several existing models for forecasting future hospitalizations and emergency department (ED) visits of patients with asthma.
| Model | Prediction target | Number of data instances | Number of features the model adopted | Classification algorithm | Sensitivity (%) | Specificity (%) | PPVa (%) | NPVb (%) | AUCc |
| Our final UWM model | Asthma hospital encounters | 82,888 | 71 | XGBoostd | 70.2 | 90.91 | 10.45 | 99.51 | 0.902 |
| Our Intermountain Healthcare model [23] | Asthma hospital encounters | 334,564 | 142 | XGBoost | 53.69 | 91.93 | 22.65 | 97.83 | 0.859 |
| Loymans et al [9] | Asthma exacerbation | 611 | 7 | Logistic regression | —e | — | — | — | 0.8 |
| Schatz et al [10] | Asthma-induced hospitalization in children | 4197 | 5 | Logistic regression | 43.9 | 89.8 | 5.6 | 99.1 | 0.781 |
| Schatz et al [10] | Asthma-induced hospitalization in adults | 6904 | 3 | Logistic regression | 44.9 | 87 | 3.9 | 99.3 | 0.712 |
| Eisner et al [11] | Asthma-induced hospitalization | 2858 | 1 | Logistic regression | — | — | — | — | 0.689 |
| Eisner et al [11] | Asthma-induced ED visit | 2415 | 3 | Logistic regression | — | — | — | — | 0.751 |
| Sato et al [12] | Severe asthma exacerbation | 78 | 3 | Classification and regression tree | — | — | — | — | 0.625 |
| Miller et al [14] | Asthma hospital encounters | 2821 | 17 | Logistic regression | — | — | — | — | 0.81 |
| Yurk et al [16] | Lost day or hospital encounters for asthma | 4888 | 11 | Logistic regression | 77 | 63 | 82 | 56 | 0.78 |
| Lieu et al [2] | Asthma-induced hospitalization | 16,520 | 7 | Proportional-hazards regression | — | — | — | — | 0.79 |
| Lieu et al [2] | Asthma-induced ED visit | 16,520 | 7 | Proportional-hazards regression | — | — | — | — | 0.69 |
| Lieu et al [18] | Asthma hospital encounters | 7141 | 4 | Classification and regression tree | 49 | 83.6 | 18.5 | — | — |
| Schatz et al [19] | Asthma hospital encounters | 14,893 | 4 | Logistic regression | 25.4 | 92 | 22 | 93.2 | 0.614 |
| Forno et al [21] | Severe asthma exacerbation | 615 | 17 | Scoring | — | — | — | — | 0.75 |
| Xiang et al [22] | Asthma exacerbation | 31,433 | — | Recurrent neural network | — | — | — | — | 0.70 |
aPPV: positive predictive value.
bNPV: negative predictive value.
cAUC: area under the receiver operating characteristic curve.
dXGBoost: extreme gradient boosting.
eThe initial paper showing the model did not give the performance measure.