Static and multivariate-temporal attentive fusion transformer for readmission risk prediction

TL;DR

This paper introduces SMTAFormer, a novel transformer-based model that effectively fuses static and multivariate temporal data to improve short-term ICU readmission risk prediction, outperforming existing methods.

Contribution

The paper proposes a new static and multivariate-temporal attentive fusion transformer (SMTAFormer) that enhances feature representation and fusion for ICU readmission prediction.

Findings

SMTAFormer achieves up to 86.6% accuracy.

AUC of SMTAFormer reaches 0.717.

Outperforms state-of-the-art methods in readmission prediction.

Abstract

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.