Table 2 Prediction performance of the reference, and machine learning models in infants hospitalized for bronchiolitis.

From: Machine learning-based prediction of acute severity in infants hospitalized for bronchiolitis: a multicenter prospective study

Outcomes and models

AUC

P-valuea

NRIb

P-valueb

Sensitivity

Specificity

PPV

NPV

Positive pressure ventilation outcome

Reference model

0.62 (0.53–0.70)

Reference

Reference

Reference

0.62 (0.49–0.75)

0.57 (0.54–0.60)

0.075 (0.054–0.097)

0.96 (0.95–0.97)

Logistic regression with Lasso regularization

0.88 (0.84–0.93)

< 0.001

1.09 (0.87–1.32)

< 0.001

0.84 (0.73–0.93)

0.79 (0.77–0.82)

0.19 (0.14–0.24)

0.99 (0.99–0.99)

Logistic regression with elastic net regularization

0.89 (0.85–0.92)

< 0.001

1.05 (0.82–1.28)

< 0.001

0.89 (0.80–0.96)

0.73 (0.70–0.75)

0.15 (0.11–0.18)

0.99 (0.99–0.99)

Random forest

0.89 (0.85–0.92)

< 0.001

1.17 (0.96–1.38)

< 0.001

0.85 (0.75–0.95)

0.74 (0.71–0.76)

0.15 (0.12–0.21)

0.99 (0.99–0.99)

Gradient boosted decision tree

0.88 (0.84–0.93)

< 0.001

1.08 (0.84–1.33)

< 0.001

0.89 (0.80–0.96)

0.77 (0.75–0.80)

0.17 (0.08–0.21)

0.99 (0.99–0.99)

Intensive treatment outcome

Reference model

0.62 (0.57–0.67)

Reference

Reference

Reference

0.58 (0.55–0.62)

0.58 (0.50–0.66)

0.21 (0.18–0.24)

0.88 (0.86–0.89)

Logistic regression with Lasso regularization

0.79 (0.76–0.83)

< 0.001

0.68 (0.52–0.84)

< 0.001

0.75 (0.69–0.82)

0.70 (0.66–0.73)

0.31 (0.26–0.38)

0.94 (0.93–0.94)

Logistic regression with elastic net regularization

0.80 (0.76–0.83)

< 0.001

0.58 (0.42–0.74)

< 0.001

0.72 (0.64–0.79)

0.74 (0.71–0.77)

0.33 (0.28–0.41)

0.93 (0.92–0.94)

Random forest

0.79 (0.75–0.84)

< 0.001

0.70 (0.55–0.86)

< 0.001

0.70 (0.63–0.77)

0.78 (0.76–0.81)

0.37 (0.29–0.45)

0.93 (0.92–0.94)

Gradient boosted decision tree

0.79 (0.75–0.84)

< 0.001

0.72 (0.57–0.87)

< 0.001

0.74 (0.67–0.80)

0.74 (0.71–0.77)

0.33 (0.26–0.42)

0.93 (0.92–0.94)

  1. AUC area under the receiver-operating-characteristic curve, NRI net reclassification improvement, PPV positive predictive value, NPV negative predictive value.
  2. aP-value was calculated to compare area-under-the-curve of the reference model with that of each machine model.
  3. bWe used continuous NRI and its P-value.
Back to article page