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Table 4 Performance (AUC) of predicting unplanned readmissions following the unplanned discharges

From: A framework for feature extraction from hospital medical data with applications in risk prediction

 

BASELINE (95% CI)

  
 

Period

1 M

3Y

MR (95% CI)

MR + Comorbidities (95% CI)

COPD

     

1 M

0.57 (0.55,0.60)

0.60 (0.57,0.63)

0.730 (0.695,0.766)

0.730 (0.695,0.766)

2 M

0.59 (0.56,0.61)

0.60 (0.57,0.62)

0.719 (0.689,0.750)

0.719 (0.689,0.750)

3 M

0.58 (0.56,0.61)

0.60 (0.58,0.63)

0.719 (0.692,0.746)

0.720 (0.693,0.746)

6 M

0.59 (0.57,0.61)

0.61 (0.59,0.64)

0.724 (0.703,0.746)

0.724 (0.702,0.745)

12 M

0.60 (0.57,0.62)

0.62 (0.59,0.64)

0.720 (0.701,0.739)

0.720 (0.701,0.739)

Diabetes

     

1 M

0.60 (0.57,0.62)

0.60 (0.58,0.63)

0.708 (0.674,0.741)

0.704 (0.670,0.738)

2 M

0.61 (0.59,0.63)

0.63 (0.61,0.65)

0.718 (0.692,0.744)

0.718 (0.692,0.743)

3 M

0.60 (0.58,0.622)

0.63 (0.61,0.65)

0.724 (0.703,0.745)

0.724 (0.703,0.745)

6 M

0.62 (0.60,0.633)

0.64 (0.62,0.66)

0.714 (0.697,0.731)

0.715 (0.698,0.732)

12 M

0.64 (0.62,0.653)

0.66 (0.64,0.68)

0.718 (0.705,0.732)

0.718 (0.704,0.732)

Mental disorders

     

1 M

0.56 (0.53,0.59)

0.57 (0.54,0.60)

0.748 (0.709,0.787)

0.747 (0.708,0.786)

2 M

0.58 (0.55,0.61)

0.60 (0.57,0.62)

0.756 (0.727,0.784)

0.756 (0.728,0.785)

3 M

0.59 (0.57,0.62)

0.60 (0.58,0.63)

0.738 (0.713,0.764)

0.737 (0.711,0.762)

6 M

0.61 (0.59,0.64)

0.63 (0.61,0.65)

0.718 (0.697,0.740)

0.718 (0.696,0.739)

12 M

0.65 (0.63,0.67)

0.66 (0.64,0.68)

0.713 (0.694,0.732)

0.713 (0.694,0.732)

Pneumonia

     

1 M

0.58 (0.55,0.60)

0.61 (0.59,0.63)

0.749 (0.717,0.782)

0.750 (0.718,0.782)

2 M

0.61 (0.59,0.63)

0.66 (0.64,0.68)

0.753 (0.729,0.777)

0.756 (0.733,0.780)

3 M

0.62 (0.60,0.64)

0.67 (0.65,0.68)

0.760 (0.739,0.780)

0.762 (0.742,0.782)

6 M

0.64 (0.62,0.66)

0.68 (0.67,0.70)

0.748 (0.731,0.764)

0.749 (0.733,0.765)

12 M

0.65 (0.63,0.67)

0.70 (0.68,0.71)

0.744 (0.731,0.758)

0.747 (0.733,0.761)

  1. AUC stands for Area Under ROC Curve; Feature sets are Elixhauser comorbidities as baselines, automatically extracted features from medical records (MR), and the combination of MR and comorbidities.