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Table 4 Long length of stay 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 \(T + S\) 0.668 (0.658, 0.678) 0.735 (0.732, 0.738) 0.615 (0.611, 0.619) 1
LR U 0.686 (0.683, 0.689) 0.736 (0.732, 0.740) 0.614 (0.610, 0.618) 0.5643
LR \(U + T + S\) 0.703 (0.699, 0.707) 0.773 (0.770, 0.776) 0.642 (0.637, 0.647) < 0.001
RF \(T + S\) 0.523 (0.462, 0.584) 0.695 (0.689, 0.701) 0.586 (0.577, 0.595) < 0.001
RF U 0.568 (0.479, 0.657) 0.651 (0.642, 0.660) 0.559 (0.553, 0.565) < 0.001
RF \(U + T + S\) 0.537 (0.533, 0.541) 0.718 (0.714, 0.722) 0.597 (0.591, 0.603) < 0.001
Deep models Fusion-CNN \(T + S\) 0.674 (0.667, 0.681) 0.748 (0.745, 0.751) 0.640 (0.635, 0.645) < 0.001
Fusion-CNN U 0.695 (0.683, 0.707) 0.742 (0.741, 0.743) 0.635 (0.632, 0.638) < 0.001
Fusion-CNN \(U + T + S\) 0.725 (0.718, 0.732) 0.784 (0.781, 0.787) 0.662 (0.658, 0.666) < 0.001
Fusion-LSTM \(T + S\) 0.690 (0.684, 0.696) 0.757 (0.756, 0.758) 0.644 (0.643, 0.645) < 0.001
Fusion-LSTM U 0.702 (0.697, 0.707) 0.746 (0.745, 0.747) 0.637 (0.634, 0.640) < 0.001
Fusion-LSTM \(U + T + S\) 0.716 (0.711, 0.721) 0.778 (0.776, 0.780) 0.660 (0.657, 0.663) < 0.001
  1. The bold in the table is maximum values of that evaluation metrics