Table 2 In-hospital mortality prediction on MIMIC-III. U, T, S represents unstructured data, temporal signals, and static information respectively

Baseline models

LR

\(T + S\)

0.341 (0.325, 0.357)

0.805 (0.799, 0.811)

0.188 (0.173, 0.203)

1

LR

U

0.373 (0.358, 0.388)

0.825 (0.817, 0.833)

0.210 (0.200, 0.220)

< 0.001

LR

\(U + T + S\)

0.395 (0.380, 0.410)

0.862 (0.859, 0.865)

0.230 (0.217, 0.243)

< 0.001

RF

\(T + S\)

0.349 (0.325, 0.373)

0.735 (0.720, 0.750)

0.181 (0.157, 0.205)

< 0.001

RF

U

0.255 (0.236, 0.274)

0.665 (0.657, 0.673)

0.134 (0.126, 0.142)

< 0.001

RF

\(U + T + S\)

0.349 (0.331, 0.367)

0.735 (0.724, 0.746)

0.181 (0.163, 0.199)

< 0.001

Deep models

Fusion-CNN

\(T + S\)

0.346 (0.330, 0.362)

0.827 (0.823, 0.831)

0.194 (0.184, 0.204)

< 0.001

Fusion-CNN

U

0.358 (0.341, 0.375)

0.826 (0.825, 0.827)

0.201 (0.198, 0.204)

< 0.001

Fusion-CNN

\(U + T + S\)

0.398 (0.378, 0.418)

0.870 (0.866, 0.874)

0.233 (0.220, 0.246)

< 0.001

Fusion-LSTM

\(T + S\)

0.374 (0.365, 0.383)

0.837 (0.834, 0.840)

0.211 (0.207, 0.215)

< 0.001

Fusion-LSTM

U

0.372 (0.352, 0.392)

0.828 (0.824, 0.832)

0.209 (0.207, 0.211)

< 0.001

Fusion-LSTM

\(U + T + S\)

0.424 (0.419, 0.429)

0.871 (0.868, 0.874)

0.250 (0.241, 0.259)

< 0.001