Predicting the risk of mortality in ICU patients based on dynamic graph attention network of patient similarity

Manfu Ma; Penghui Sun; Yong Li; Weilong Huo; Manfu Ma; Penghui Sun; Yong Li; Weilong Huo

doi:10.3934/mbe.2023685

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 8: 15326-15344. doi: 10.3934/mbe.2023685

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Predicting the risk of mortality in ICU patients based on dynamic graph attention network of patient similarity

1.
College of Computer Science and Engineering, Northwest Normal University, 967 Anning East Road, Lanzhou 730070, China
2.
College of Traffic and Transportation, Lanzhou Jiaotong University, 88 Anning West Road, Lanzhou 730070, China

Academic Editor: Vladimir Mityushev

Received: 01 June 2023 Revised: 14 July 2023 Accepted: 17 July 2023 Published: 21 July 2023

Predicting the risk of mortality of hospitalized patients in the ICU is essential for timely identification of high-risk patients and formulate and adjustment of treatment strategies when patients are hospitalized. Traditional machine learning methods usually ignore the similarity between patients and make it difficult to uncover the hidden relationships between patients, resulting in poor accuracy of prediction models. In this paper, we propose a new model named PS-DGAT to solve the above problem. First, we construct a patient-weighted similarity network by calculating the similarity of patient clinical data to represent the similarity relationship between patients; second, we fill in the missing features and reconstruct the patient similarity network based on the data of neighboring patients in the network; finally, from the reconstructed patient similarity network after feature completion, we use the dynamic attention mechanism to extract and learn the structural features of the nodes to obtain a vector representation of each patient node in the low-dimensional embedding The vector representation of each patient node in the low-dimensional embedding space is used to achieve patient mortality risk prediction. The experimental results show that the accuracy is improved by about 1.8% compared with the basic GAT and about 8% compared with the traditional machine learning methods.
- patient similarity network,
- graph neural network,
- missing value filling,
- mortality risk prediction
Citation: Manfu Ma, Penghui Sun, Yong Li, Weilong Huo. Predicting the risk of mortality in ICU patients based on dynamic graph attention network of patient similarity[J]. Mathematical Biosciences and Engineering, 2023, 20(8): 15326-15344. doi: 10.3934/mbe.2023685

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Abstract

Predicting the risk of mortality of hospitalized patients in the ICU is essential for timely identification of high-risk patients and formulate and adjustment of treatment strategies when patients are hospitalized. Traditional machine learning methods usually ignore the similarity between patients and make it difficult to uncover the hidden relationships between patients, resulting in poor accuracy of prediction models. In this paper, we propose a new model named PS-DGAT to solve the above problem. First, we construct a patient-weighted similarity network by calculating the similarity of patient clinical data to represent the similarity relationship between patients; second, we fill in the missing features and reconstruct the patient similarity network based on the data of neighboring patients in the network; finally, from the reconstructed patient similarity network after feature completion, we use the dynamic attention mechanism to extract and learn the structural features of the nodes to obtain a vector representation of each patient node in the low-dimensional embedding The vector representation of each patient node in the low-dimensional embedding space is used to achieve patient mortality risk prediction. The experimental results show that the accuracy is improved by about 1.8% compared with the basic GAT and about 8% compared with the traditional machine learning methods.

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