"A Deep Learning Approach for Inpatient Length of Stay and Mortality Pr" by Junde Chen, Trudi Di Qi et al.
 

A Deep Learning Approach for Inpatient Length of Stay and Mortality Prediction

Document Type

Article

Publication Date

10-17-2023

Abstract

Purpose

Accurate prediction of the Length of Stay (LoS) and mortality in the Intensive Care Unit (ICU) is crucial for effective hospital management, and it can assist clinicians for real-time demand capacity (RTDC) administration, thereby improving healthcare quality and service levels.

Methods

This paper proposes a novel one-dimensional (1D) multi-scale convolutional neural network architecture, namely 1D-MSNet, to predict inpatients’ LoS and mortality in ICU. First, a 1D multi-scale convolution framework is proposed to enlarge the convolutional receptive fields and enhance the richness of the convolutional features. Following the convolutional layers, an atrous causal spatial pyramid pooling (SPP) module is incorporated into the networks to extract high-level features. The optimized Focal Loss (FL) function is combined with the synthetic minority over-sampling technique (SMOTE) to mitigate the imbalanced-class issue.

Results

On the MIMIC-IV v1.0 benchmark dataset, the proposed approach achieves the optimum R-Square and RMSE values of 0.57 and 3.61 for the LoS prediction, and the highest test accuracy of 97.73% for the mortality prediction.

Conclusion

The proposed approach presents a superior performance in comparison with other state-of-the-art, and it can effectively perform the LoS and mortality prediction tasks.

Comments

This article was originally published in Journal of Biomedical Informatics, volume147, in 2023. https://doi.org/10.1016/j.jbi.2023.104526

Copyright

Elsevier

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