Table 2 Prediction accuracy of each task made at different time points

From: Scalable and accurate deep learning with electronic health records

  Hospital A Hospital B
Inpatient mortality, AUROC a (95% CI)
24 h before admission 0.87 (0.85–0.89) 0.81 (0.79–0.83)
At admission 0.90 (0.88–0.92) 0.90 (0.86–0.91)
24 h after admission 0.95 (0.94–0.96) 0.93 (0.92–0.94)
Baseline (aEWSb) at 24 h after admission 0.85 (0.81–0.89) 0.86 (0.83–0.88)
30-day readmission, AUROC (95% CI)
At admission 0.73 (0.71–0.74) 0.72 (0.71–0.73)
At 24 h after admission 0.74 (0.72–0.75) 0.73 (0.72–0.74)
At discharge 0.77 (0.75–0.78) 0.76 (0.75–0.77)
Baseline (mHOSPITALc) at discharge 0.70 (0.68–0.72) 0.68 (0.67–0.69)
Length of stay at least 7 days, AUROC (95% CI)
At admission 0.81 (0.80–0.82) 0.80 (0.80–0.81)
At 24 h after admission 0.86 (0.86–0.87) 0.85 (0.85–0.86)
Baseline (Liud) at 24 h after admission 0.76 (0.75–0.77) 0.74 (0.73–0.75)
Discharge diagnoses (weighted AUROC)
At admission 0.87 0.86
At 24 h after admission 0.89 0.88
At discharge 0.90 0.90
  1. aArea under the receiver operator curve
  2. bAugmented Early Warning System score
  3. cModified HOSPITAL score for readmission
  4. dModified Liu score for long length of stay
  5. The bold values indicate the highest area-under-the-receiver-operator-curve for each prediction task