Prediction of Omicron Virus Using Combined Extended Convolutional and Recurrent Neural Networks Technique on CT-Scan Images

Gupta, Anand Kumar; Srinivasulu, Asadi; Hiran, Kamal Kant; Sreenivasulu, Goddindla; Rajeyyagari, Sivaram; Subramanyam, Madhusudhana

doi:10.1155/2022/1525615

Cited by 12 publications

(1 citation statement)

References 37 publications

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“…Using attention-based recurrent neural networks, one method to predict mutations within the influenza A virus over time was studied by Yin R [30] . Gupta AK [31] predicted the omicron virus on CT scan pictures by employing an integrated extended convolutional and recurrent neural networks approach. RNNs [32] can also be used to predict medication resistance in the human immunodeficiency virus (HIV).…”

Section: Introductionmentioning

confidence: 99%

Recurrent neural network for the dynamics of Zika virus spreading

Nisar,

Anjum,

Raja

et al. 2024

AIMSPH

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<abstract> <p>Recurrent Neural Networks (RNNs), a type of machine learning technique, have recently drawn a lot of interest in numerous fields, including epidemiology. Implementing public health interventions in the field of epidemiology depends on efficient modeling and outbreak prediction. Because RNNs can capture sequential dependencies in data, they have become highly effective tools in this field. In this paper, the use of RNNs in epidemic modeling is examined, with a focus on the extent to which they can handle the inherent temporal dynamics in the spread of diseases. The mathematical representation of epidemics requires taking time-dependent variables into account, such as the rate at which infections spread and the long-term effects of interventions. The goal of this study is to use an intelligent computing solution based on RNNs to provide numerical performances and interpretations for the SEIR nonlinear system based on the propagation of the Zika virus (SEIRS-PZV) model. The four patient dynamics, namely susceptible patients S(y), exposed patients admitted in a hospital E(y), the fraction of infective individuals I(y), and recovered patients R(y), are represented by the epidemic version of the nonlinear system, or the SEIR model. SEIRS-PZV is represented by ordinary differential equations (ODEs), which are then solved by the Adams method using the Mathematica software to generate a dataset. The dataset was used as an output for the RNN to train the model and examine results such as regressions, correlations, error histograms, etc. For RNN, we used 100% to train the model with 15 hidden layers and a delay of 2 seconds. The input for the RNN is a time series sequence from 0 to 5, with a step size of 0.05. In the end, we compared the approximated solution with the exact solution by plotting them on the same graph and generating the absolute error plot for each of the 4 cases of SEIRS-PZV. Predictions made by the model appeared to be become more accurate when the mean squared error (MSE) decreased. An increased fit to the observed data was suggested by this decrease in the MSE, which suggested that the variance between the model's predicted values and the actual values was dropping. A minimal absolute error almost equal to zero was obtained, which further supports the usefulness of the suggested strategy. A small absolute error shows the degree to which the model's predictions matches the ground truth values, thus indicating the level of accuracy and precision for the model's output.</p> </abstract>

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Section: Introductionmentioning

confidence: 99%

Recurrent neural network for the dynamics of Zika virus spreading

Nisar,

Anjum,

Raja

et al. 2024

AIMSPH

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Predicting omicron pneumonia severity and outcome: a single-center study in Hangzhou, China

Cao

Miao

et al. 2023

Front. Med.

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BackgroundIn December 2022, there was a large Omicron epidemic in Hangzhou, China. Many people were diagnosed with Omicron pneumonia with variable symptom severity and outcome. Computed tomography (CT) imaging has been proven to be an important tool for COVID-19 pneumonia screening and quantification. We hypothesized that CT-based machine learning algorithms can predict disease severity and outcome in Omicron pneumonia, and we compared its performance with the pneumonia severity index (PSI)-related clinical and biological features.MethodsOur study included 238 patients with the Omicron variant who have been admitted to our hospital in China from 15 December 2022 to 16 January 2023 (the first wave after the dynamic zero-COVID strategy stopped). All patients had a positive real-time polymerase chain reaction (PCR) or lateral flow antigen test for SARS-CoV-2 after vaccination and no previous SARS-CoV-2 infections. We recorded patient baseline information pertaining to demographics, comorbid conditions, vital signs, and available laboratory data. All CT images were processed with a commercial artificial intelligence (AI) algorithm to obtain the volume and percentage of consolidation and infiltration related to Omicron pneumonia. The support vector machine (SVM) model was used to predict the disease severity and outcome.ResultsThe receiver operating characteristic (ROC) area under the curve (AUC) of the machine learning classifier using PSI-related features was 0.85 (accuracy = 87.40%, p < 0.001) for predicting severity while that using CT-based features was only 0.70 (accuracy = 76.47%, p = 0.014). If combined, the AUC was not increased, showing 0.84 (accuracy = 84.03%, p < 0.001). Trained on outcome prediction, the classifier reached the AUC of 0.85 using PSI-related features (accuracy = 85.29%, p < 0.001), which was higher than using CT-based features (AUC = 0.67, accuracy = 75.21%, p < 0.001). If combined, the integrated model showed a slightly higher AUC of 0.86 (accuracy = 86.13%, p < 0.001). Oxygen saturation, IL-6, and CT infiltration showed great importance in both predicting severity and outcome.ConclusionOur study provided a comprehensive analysis and comparison between baseline chest CT and clinical assessment in disease severity and outcome prediction in Omicron pneumonia. The predictive model accurately predicts the severity and outcome of Omicron infection. Oxygen saturation, IL-6, and infiltration in chest CT were found to be important biomarkers. This approach has the potential to provide frontline physicians with an objective tool to manage Omicron patients more effectively in time-sensitive, stressful, and potentially resource-constrained environments.

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State-of-the-Art Review on the Models, Techniques, and Datasets to Diagnose COVID-19 Disease

Vetrithangam

Pegada²,

Himabindu³

et al. 2023

Handbook of Research on AI and Knowledge Engineering for Real-Time Business Intelligence

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The present corona virus disease outbreak of 2019 is a rapidly spreading illness caused by the novel serious acute respiratory syndrome coronavirus2 (SARS-Cov2). France has the highest rates of infection, morbidity, and mortality, and is among the nations most impacted by the disease, along with the United States, India, Brazil, and Russia. Since early January 2022, thousands of articles have been published on COVID-19. The majority of these articles agreed with descriptions of the mode of transmission, spread, duration, and severity of the illness; models or techniques used to diagnose the COVID-19 disease; and vaccine status in various locations. Thus, this review completely discusses the highest analytical aspects of COVID-19, including various classification, segmentation, prediction, and feature selection techniques to diagnose, detect, and predict the Covid-19 disease. This review chapter will surely help researchers to choose the techniques and datasets for effective diagnosis and evaluation.

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Prediction of Omicron Virus Using Combined Extended Convolutional and Recurrent Neural Networks Technique on CT-Scan Images

Cited by 12 publications

References 37 publications

Recurrent neural network for the dynamics of Zika virus spreading

Recurrent neural network for the dynamics of Zika virus spreading

Predicting omicron pneumonia severity and outcome: a single-center study in Hangzhou, China

State-of-the-Art Review on the Models, Techniques, and Datasets to Diagnose COVID-19 Disease

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