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Table 6 30-day readmission prediction on MIMIC-III. U, T, S represents unstructured data, temporal signals, and static information respectively

From: Combining structured and unstructured data for predictive models: a deep learning approach

  Model Model inputs F1 AUROC AUPRC P value
Baseline models LR \(T + S\) 0.144 (0.136, 0.152) 0.649 (0.646, 0.652) 0.071 (0.062, 0.080) 1
LR U 0.142 (0.133, 0.151) 0.638 (0.634, 0.642) 0.070 (0.056, 0.084) < 0.001
LR \(U + T + S\) 0.144 (0.137, 0.151) 0.660 (0.657, 0.663) 0.072 (0.059, 0.085) < 0.001
RF \(T + S\) 0.123 (0.113, 0.133) 0.575 (0.559, 0.591) 0.060 (0.054, 0.066) < 0.001
RF U 0.117 (0.105, 0.129) 0.557 (0.539, 0.575) 0.059 (0.056, 0.062) < 0.001
RF \(U + T + S\) 0.118 (0.111, 0.125) 0.560 (0.543, 0.577) 0.059 (0.056, 0.062) < 0.001
Deep models Fusion-CNN \(T + S\) 0.155 (0.146, 0.164) 0.657 (0.650, 0.664) 0.077 (0.073, 0.081) 0.0208
Fusion-CNN U 0.163 (0.160, 0.166) 0.663 (0.660, 0.666) 0.078 (0.077, 0.079) < 0.001
Fusion-CNN \(U + T + S\) 0.164 (0.161, 0.167) 0.671 (0.668, 0.674) 0.080 (0.076, 0.084) < 0.001
Fusion-LSTM \(T + S\) 0.149 (0.146, 0.152) 0.653 (0.651, 0.655) 0.074 (0.071, 0.077) 0.0158
Fusion-LSTM U 0.158 (0.154, 0.162) 0.641 (0.635, 0.647) 0.075 (0.072, 0.078) 0.0076
Fusion-LSTM \(U + T + S\) 0.160 (0.151, 0.169) 0.674 (0.672, 0.676) 0.079 (0.076, 0.082) < 0.001
  1. The bold in the table is maximum values of that evaluation metrics