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Table 5 P value matrix of various model performances (AUROC) for long length of stay prediction. 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

  LR RF Fusion-CNN Fusion-LSTM
T + S U U + T + S T + S U U + T + S T + S U U + T + S T + S U U + T + S
LR \(T + S\) 1 0.5643 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001
U 0.5643 1 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.0024 < 0.001 < 0.001 < 0.001 < 0.001
\(U + T + S\) < 0.001 < 0.001 1 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.0073
RF \(T + S\) < 0.001 < 0.001 < 0.001 1 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001
U < 0.001 < 0.001 < 0.001 < 0.001 1 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001
\(U + T + S\) < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 1 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001
Fusion-CNN \(T + S\) < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 1 < 0.001 < 0.001 < 0.001 0.1134 < 0.001
U < 0.001 0.0024 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 1 < 0.001 < 0.001 < 0.001 < 0.001
\(U + T + S\) < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 1 < 0.001 < 0.001 0.002
Fusion-LSTM \(T + S\) < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 1 < 0.001 < 0.001
U < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.1134 < 0.001 < 0.001 < 0.001 1 < 0.001
\(U + T + S\) < 0.001 < 0.001 0.0073 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.002 < 0.001 < 0.001 1