Integrated Convolutional and Recurrent Neural Networks for Health Risk Prediction using Patient Journey Data with Many Missing Values

Liu, Yuxi; Qin, Shaowen; Yepes, Antonio Jimeno; Shao, Wei; Zhang, Zhenhao; Salim, Flora D.

doi:10.1109/bibm55620.2022.9995048

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2024

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Cited by 3 publications

References 29 publications

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Fine-grained Patient Similarity Measuring using Contrastive Graph Similarity Networks

Liu,

Zhang,

Qin

et al. 2024

2024 IEEE 12th International Conference on Healthcare Informatics (ICHI)

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Fine-grained Patient Similarity Measuring using Contrastive Graph Similarity Networks

Liu,

Zhang,

Qin

et al. 2024

2024 IEEE 12th International Conference on Healthcare Informatics (ICHI)

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A Multi-Graph Fusion Framework for Patient Representation Learning

Liu,

Zhang,

Qin

et al. 2024

2024 IEEE 12th International Conference on Healthcare Informatics (ICHI)

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Machine learning approaches toward an understanding of acute kidney injury: current trends and future directions

Jeong,

Cho,

Ahn

et al. 2024

Korean J Intern Med

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Acute kidney injury (AKI) is a significant health challenge associated with adverse patient outcomes and substantial economic burdens. Many authors have sought to prevent and predict AKI. Here, we comprehensively review recent advances in the use of artificial intelligence (AI) to predict AKI, and the associated challenges. Although AI may detect AKI early and predict prognosis, integration of AI-based systems into clinical practice remains challenging. It is difficult to identify AKI patients using retrospective data; information preprocessing and the limitations of existing models pose problems. It is essential to embrace standardized labeling criteria and to form international multi-institutional collaborations that foster high-quality data collection. Additionally, existing constraints on the deployment of evolving AI technologies in real-world healthcare settings and enhancement of the reliabilities of AI outputs are crucial. Such efforts will improve the clinical applicability, performance, and reliability of AKI Clinical Support Systems, ultimately enhancing patient prognoses.

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Integrated Convolutional and Recurrent Neural Networks for Health Risk Prediction using Patient Journey Data with Many Missing Values

Cited by 3 publications

References 29 publications

Fine-grained Patient Similarity Measuring using Contrastive Graph Similarity Networks

Fine-grained Patient Similarity Measuring using Contrastive Graph Similarity Networks

A Multi-Graph Fusion Framework for Patient Representation Learning

Machine learning approaches toward an understanding of acute kidney injury: current trends and future directions

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