  Background: Accurate short-term readmission prediction of ICU patients is
significant in improving the efficiency of resource assignment by assisting
physicians in making discharge decisions. Clinically, both individual static
static and multivariate temporal data collected from ICU monitors play critical
roles in short-term readmission prediction. Informative static and multivariate
temporal feature representation capturing and fusion present challenges for
accurate readmission prediction. Methods:We propose a novel static and
multivariate-temporal attentive fusion transformer (SMTAFormer) to predict
short-term readmission of ICU patients by fully leveraging the potential of
demographic and dynamic temporal data. In SMTAFormer, we first apply an MLP
network and a temporal transformer network to learn useful static and temporal
feature representations, respectively. Then, the well-designed static and
multivariate temporal feature fusion module is applied to fuse static and
temporal feature representations by modeling intra-correlation among
multivariate temporal features and constructing inter-correlation between
static and multivariate temporal features. Results: We construct a readmission
risk assessment (RRA) dataset based on the MIMIC-III dataset. The extensive
experiments show that SMTAFormer outperforms advanced methods, in which the
accuracy of our proposed method is up to 86.6%, and the area under the receiver
operating characteristic curve (AUC) is up to 0.717. Conclusion: Our proposed
SMTAFormer can efficiently capture and fuse static and multivariate temporal
feature representations. The results show that SMTAFormer significantly
improves the short-term readmission prediction performance of ICU patients
through comparisons to strong baselines.
