Table 2.

Acute kidney injury predictive performance in the testing dataset with optimized F1.

Model	Precisiona	Recallb	F1c	AUCd (95% CI)
Logistic regression	0.274	0.189	0.224	0.789 (0.752-0.827)
RNNe (GRUf)	0.286	0.222	0.250	0.800 (0.764-0.836)
BRNNg (BGRUh)	0.309	0.233	0.266	0.797 (0.761-0.833)
Proposed TITVi model	0.397	0.256	0.311	0.814 (0.780-0.848)

^aPrecision: true positive / (all cases predicted at risk of acute kidney injury).

^bRecall: true positive / (all cases that eventually developed acute kidney injury).

^cF1 score: 2 × [(recall × precision) / (recall + precision)].

^dAUC: area under receiver operating characteristic curve.

^eRNN: recurrent neural network.

^fGRU: gated recurrent unit.

^gBRNN: bidirectional recurrent neural network.

^hBGRU: bidirectional gated recurrent unit.

ⁱTITV: time-invariant and time-variant feature importance.