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Review
. 2023 Mar 1:2023:1102715.
doi: 10.1155/2023/1102715. eCollection 2023.

Next Generation Infectious Diseases Monitoring Gages via Incremental Federated Learning: Current Trends and Future Possibilities

Iqra Javed  1 , Uzair Iqbal  2 , Muhammad Bilal  3 , Basit Shahzad  1 , Tae-Sun Chung  4 , Muhammad Attique  5
Affiliations
Review

Next Generation Infectious Diseases Monitoring Gages via Incremental Federated Learning: Current Trends and Future Possibilities

Iqra Javed et al. Comput Intell Neurosci. .

Abstract

Infectious diseases are always alarming for the survival of human life and are a key concern in the public health domain. Therefore, early diagnosis of these infectious diseases is a high demand for modern-era healthcare systems. Novel general infectious diseases such as coronavirus are infectious diseases that cause millions of human deaths across the globe in 2020. Therefore, early, robust recognition of general infectious diseases is the desirable requirement of modern intelligent healthcare systems. This systematic study is designed under Kitchenham guidelines and sets different RQs (research questions) for robust recognition of general infectious diseases. From 2018 to 2021, four electronic databases, IEEE, ACM, Springer, and ScienceDirect, are used for the extraction of research work. These extracted studies delivered different schemes for the accurate recognition of general infectious diseases through different machine learning techniques with the inclusion of deep learning and federated learning models. A framework is also introduced to share the process of detection of infectious diseases by using machine learning models. After the filtration process, 21 studies are extracted and mapped to defined RQs. In the future, early diagnosis of infectious diseases will be possible through wearable health monitoring cages. Moreover, these gages will help to reduce the time and death rate by detection of severe diseases at starting stage.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Use of infectious diseases monitoring gages.
Figure 2
Figure 2
Study filtration and selection process.
Figure 3
Figure 3
Structural diagram of the study extraction process.
Figure 4
Figure 4
Structure of the convolutional neural network (feed-forward).
Figure 5
Figure 5
The internal flow of the CNN model.
Figure 6
Figure 6
RNN structural diagram.
Figure 7
Figure 7
Sequential RNN model.
Figure 8
Figure 8
Reinforcement learning basic diagram.
Figure 9
Figure 9
The architecture of federated learning.
Figure 10
Figure 10
Incremental federated learning integrates into current digital healthcare systems.
Figure 11
Figure 11
Healthcare model overview using distributed federated learning.
See this image and copyright information in PMC

References

    1. Reshi A. A., Rustam F., Mehmood A., et al. An efficient CNN model for COVID-19 disease detection based on X-ray image classification. Complexity . 2021:2021–12. doi: 10.1155/2021/6621607. - DOI
    1. Choi E., Bahadori M. T., Song L., Stewart W. F., Sun J. GRAM: graph-based attention model for healthcare representation learning. Proceedings of the KDD: Proceedings. International Conference on Knowledge Discovery & Data Mining; August 2017; Halifax NS Canada. pp. 787–795. - DOI - PMC - PubMed
    1. Mastoi Q. U. A., Wah T. Y., Iqbal U. Automated diagnosis of coronary artery disease: a review and workflow. Cardiology Research and Practice . 2018;2018:9. doi: 10.1155/2018/2016282.2016282 - DOI - PMC - PubMed
    1. Felsenstein S., Herbert J. A., McNamara P. S., Hedrich C. M. COVID-19: immunology and treatment options. Clinical Immunology . 2020;215 doi: 10.1016/j.clim.2020.108448.108448 - DOI - PMC - PubMed
    1. Cunningham A. C., Goh H. P., Koh D. Treatment of COVID-19: old tricks for new challenges. Critical Care . 2020;24(1):91–97. doi: 10.1186/s13054-020-2818-6. - DOI - PMC - PubMed