COVID-19 Mortality Rate Prediction for India Using Statistical Neural Network Models

Dhamodharavadhani, S.; Rathipriya, R.; Chatterjee, Jyotir Moy

doi:10.3389/fpubh.2020.00441

Cited by 71 publications

(58 citation statements)

References 29 publications

(39 reference statements)

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“…Related works are divided into non-deep learning methods and deep learning methods. We use Cox [ 19 ], k-NN [ 16 ], SVM [ 17 ], DT [ 1 ], BPNN [ 20 ], PNN [ 21 ], RNN, LSTM and T-LSTM for COVID-19 mortality prediction. T-LSTM is our method.…”

Section: Resultsmentioning

confidence: 99%

“…It is used in [ 20 ] g PNN: Probabilistic Neural Network is a forward propagation network and does not need back propagation to optimize parameters by using Bayesian decision-making. It is used in [ 21 ] h RNN: Recurrent Neural Network have been introduced in the ‘T-LSTM’ section i LSTM: Long Short-Term Memory which we have introduced in the ‘T-LSTM’ section. Here, the hyperparameter setting is same as T-LSTM j T-LSTM: Time-aware LSTM is the model used in this paper.…”

Section: Resultsmentioning

confidence: 99%

“…LDA [14] NB [15] k-NN [16] LR [17,18] Cox [19] [17] RF [17] XGBoost [1] BPNN [17,19,20], GRNN [21], RBFNN [21], PNN [21] Basic CNN [22], Transfer CNN [23], GDCNN [24], COVID-Net [25], COVNet [26] /…”

Section: Regression Svm Decision Tree Basic Nn Cnns Rnnsmentioning

confidence: 99%

“…Deep learning methods can train the parameters with complex nonlinearity to learn the data structures and have achieved state-of-the-art in many medical prediction tasks [28][29][30]. Thus, many current works apply deep learning methods for COVID-19 prediction tasks [17,[19][20][21][22][23][24][25][26]. However, these methods either use the simple multi-layer perceptron for predicting or use the convolutional structures for image classification.…”

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confidence: 99%

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Predicting COVID-19 disease progression and patient outcomes based on temporal deep learning

Sun

Hong

Song

et al. 2021

BMC Med Inform Decis Mak

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Background The coronavirus disease 2019 (COVID-19) pandemic has caused health concerns worldwide since December 2019. From the beginning of infection, patients will progress through different symptom stages, such as fever, dyspnea or even death. Identifying disease progression and predicting patient outcome at an early stage helps target treatment and resource allocation. However, there is no clear COVID-19 stage definition, and few studies have addressed characterizing COVID-19 progression, making the need for this study evident. Methods We proposed a temporal deep learning method, based on a time-aware long short-term memory (T-LSTM) neural network and used an online open dataset, including blood samples of 485 patients from Wuhan, China, to train the model. Our method can grasp the dynamic relations in irregularly sampled time series, which is ignored by existing works. Specifically, our method predicted the outcome of COVID-19 patients by considering both the biomarkers and the irregular time intervals. Then, we used the patient representations, extracted from T-LSTM units, to subtype the patient stages and describe the disease progression of COVID-19. Results Using our method, the accuracy of the outcome of prediction results was more than 90% at 12 days and 98, 95 and 93% at 3, 6, and 9 days, respectively. Most importantly, we found 4 stages of COVID-19 progression with different patient statuses and mortality risks. We ranked 40 biomarkers related to disease and gave the reference values of them for each stage. Top 5 is Lymph, LDH, hs-CRP, Indirect Bilirubin, Creatinine. Besides, we have found 3 complications - myocardial injury, liver function injury and renal function injury. Predicting which of the 4 stages the patient is currently in can help doctors better assess and cure the patient. Conclusions To combat the COVID-19 epidemic, this paper aims to help clinicians better assess and treat infected patients, provide relevant researchers with potential disease progression patterns, and enable more effective use of medical resources. Our method predicted patient outcomes with high accuracy and identified a four-stage disease progression. We hope that the obtained results and patterns will aid in fighting the disease.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Regression Svm Decision Tree Basic Nn Cnns Rnnsmentioning

confidence: 99%

mentioning

confidence: 99%

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Predicting COVID-19 disease progression and patient outcomes based on temporal deep learning

Sun

Hong

Song

et al. 2021

BMC Med Inform Decis Mak

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“…Machine learning models, which are being widely used to predict COVID-19 incidence and mortality (Dhamodharavadhani et al. 2020 ; Iwendi et al. 2020 ; Lalmuanawma et al.…”

Section: Introductionmentioning

confidence: 99%

The impact of modelling choices on modelling outcomes: a spatio-temporal study of the association between COVID-19 spread and environmental conditions in Catalonia (Spain)

Briz‐Redón¹

2021

Stoch Environ Res Risk Assess

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The choices that researchers make while conducting a statistical analysis usually have a notable impact on the results. This fact has become evident in the ongoing research of the association between the environment and the evolution of the coronavirus disease 2019 (COVID-19) pandemic, in light of the hundreds of contradictory studies that have already been published on this issue in just a few months. In this paper, a COVID-19 dataset containing the number of daily cases registered in the regions of Catalonia (Spain) since the start of the pandemic to the end of August 2020 is analysed using statistical models of diverse levels of complexity. Specifically, the possible effect of several environmental variables (solar exposure, mean temperature, and wind speed) on the number of cases is assessed. Thus, the first objective of the paper is to show how the choice of a certain type of statistical model to conduct the analysis can have a severe impact on the associations that are inferred between the covariates and the response variable. Secondly, it is shown how the use of spatiotemporal models accounting for the nature of the data allows understanding the evolution of the pandemic in space and time. The results suggest that even though the models fitted to the data correctly capture the evolution of COVID-19 in space and time, determining whether there is an association between the spread of the pandemic and certain environmental conditions is complex, as it is severely affected by the choice of the model. Keywords COVID-19 Á Spatio-temporal models Á Environmental covariates Á Integrated nested Laplace approximation Á Space-time interaction Á Relative risk & Á lvaro Briz-Redón

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Intelligent Reclamation of Plantae Affliction Disease

Banu¹,

Ahammed

Taranum³

2021

Machine Learning Approach for Cloud Data Analytics in IoT

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COVID-19 Mortality Rate Prediction for India Using Statistical Neural Network Models

Cited by 71 publications

References 29 publications

Predicting COVID-19 disease progression and patient outcomes based on temporal deep learning

Predicting COVID-19 disease progression and patient outcomes based on temporal deep learning

The impact of modelling choices on modelling outcomes: a spatio-temporal study of the association between COVID-19 spread and environmental conditions in Catalonia (Spain)

Intelligent Reclamation of Plantae Affliction Disease

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