Independent role of CT chest scan in COVID-19 prognosis: Evidence from the machine learning classification

Bhandari, Sudhir; Singh, Shaktawat; Tak, Amit; Patel, Bhoopendra; Gupta, Jitentdra; Gupta, Kapil; Kakkar, Shivankan; Darshan, S. L. Shiva; Arora, Ayesha; Dube, Amitabh

doi:10.5937/scriptamed52-34457

Cited by 3 publications

(4 citation statements)

References 23 publications

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“…The present study considered CT score an outcome as it has an independent role in diagnosing COVID-19. (28). Furthermore, Covishield and Covaxin were equally effective in post-vaccination effects and COVID-19 related characteristics.…”

Section: Professional Articlesmentioning

confidence: 88%

Effectiveness of coronavirus disease-19 vaccination on disease transmission, hospitalization, and clinical outcomes in adults in North India

Shah¹

2021

Praxis medica

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Background: Covaxin and Covishield vaccines have rapidly rolled out in India to curb the pandemic. We aimed to test the hypothesis that COVID-19 vaccination is clinically effective. Methods: This study was conducted in the Department of Physiology at MGM College, Jaipur, India after approval from the Ethics Committee. Vaccinated and non-vaccinated groups were tested on disease transmission, hospitalization, and clinical outcomes. Researchers collected data using questionnaires circulated through google forms. The association between attributes was tested using the chi-squared test. The significance level was considered at 5%. Results: The vaccination significantly reduced disease transmission of COVID-19 [c2=4.51; p = 0.034]. However, no significant differences were seen in RTPCR positivity, chest CT findings, and hospitalization on vaccination. COVID-related symptoms and their severity were not statistically different between the two groups. Most elderly were vaccinated [c2=41.68; p < 0.001]. Most youths took one dose, while the elderly took two doses of the vaccine [c2=41.77; p < 0.001]. All age groups had similar severity of AEFIs [c2=13.22; p < 0.21]. The vaccination status across gender did not differ significantly. [c2=1.13; p < 0.288] Most males took two doses as compared to females [c2=6,57; p < 0.01]. Adverse effects post-immunization were more severe in females than males [c2=13.10; p < 0.001]. There was no association between the number of vaccine doses and the severity of AEFIs [c2=16.42; p = 0.06]. Conclusion: The present study concludes the beneficial effect of vaccination in reducing disease transmission. However, vaccination has no role in mitigating other COVID-related outcomes.

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Section: Professional Articlesmentioning

confidence: 88%

Effectiveness of coronavirus disease-19 vaccination on disease transmission, hospitalization, and clinical outcomes in adults in North India

Shah¹

2021

Praxis medica

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“…The results showed COVID-19 infection AUC of 0.790, development of ARDS at 0.781, ICU admission at 0.675, and risk of mortality at 0.759. Finally, the authors in [ 116 ] explored the role that CT imaging plays in the prognosis of COVID-19 using machine learning algorithms. A mixture of clinical and laboratory features along with CT imaging was used and reduced to seven predictors.…”

Section: Covid-19 Prediction Using Deep Learningmentioning

confidence: 99%

“…The highest performing algorithms were Ensemble Bagged Trees and Tree (Fine, Medium, and Coarse) with 98.70% and 97.40% respectively. The study in [ 116 ] used machine learning algorithms to analyze clinical and laboratory variables along with a CT severity score to predict patient prognosis for COVID-19. The primary goal was to determine the role that this CT severity score has for prognostic value in patient assessment.…”

Section: Covid-19 Prognostic and Longitudinal Modelsmentioning

confidence: 99%

A Comprehensive Review of Machine Learning Used to Combat COVID-19

et al. 2022

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Coronavirus disease (COVID-19) has had a significant impact on global health since the start of the pandemic in 2019. As of June 2022, over 539 million cases have been confirmed worldwide with over 6.3 million deaths as a result. Artificial Intelligence (AI) solutions such as machine learning and deep learning have played a major part in this pandemic for the diagnosis and treatment of COVID-19. In this research, we review these modern tools deployed to solve a variety of complex problems. We explore research that focused on analyzing medical images using AI models for identification, classification, and tissue segmentation of the disease. We also explore prognostic models that were developed to predict health outcomes and optimize the allocation of scarce medical resources. Longitudinal studies were conducted to better understand COVID-19 and its effects on patients over a period of time. This comprehensive review of the different AI methods and modeling efforts will shed light on the role that AI has played and what path it intends to take in the fight against COVID-19.

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“…Several prediction methods are available based on statistical or machine learning methods. (5)(6) (7)…”

Section: Introductionmentioning

confidence: 99%

Prediction of Type 2 Diabetes Mellitus Using Soft Computing

Tak¹,

PUNJABI²,

YADAV³

et al. 2022

Rev. Med. Mod.

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Background: Type 2 Diabetes Mellitus (DM) is another pandemic of 21 century, and its control is of immense importance. Researchers developed many predictor models using soft computing techniques. The present study developed a prediction model for Type 2 DM using machine learning classifiers. The analysis excludes plasma glucose concentration and insulin concentration as predictors to explore relationships with other predictors. Background: Methods: This cross-sectional study enrolled 108 participants aged 25 to 67 years from SMS Medical College, Jaipur (Rajasthan, India), after approval from the ethics committee. The study developed a prediction model using machine learning techniques. The classifiers used in the application include decision trees, support vector machines, K-nearest neighbors, and ensemble learning classifiers. A total of 25 predictors were collected and underwent feature reduction. The response levels include diabetes mellitus, prediabetes, and no diabetes mellitus. The models were run using three predictors and a response variable. The prediction model with the best accuracy and area under the receiver operator characteristic curve was selected. Results: The features that vary among the three groups include age, WHR, biceps skinfold thickness, total lipids, phospholipids, triglycerides, total cholesterol, LDL, VLDL, and serum creatinine, and family history of DM. After feature reduction, the age, biceps skinfold thickness, and serum creatinine were run on the Classification learner application to predict the diabetic category. The best model was subspace discriminant with accuracy, sensitivity, specificity, and AUC under the ROC curve was 62.4%, 74%, 94%, and 0.70, respectively. Conclusion: The present study concludes that age, biceps skinfold thickness, and serum creatinine combination have higher specificity in predicting type 2 DM. The study emphasized the selection of appropriate predictors along with newer machine learning algorithms.

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Independent role of CT chest scan in COVID-19 prognosis: Evidence from the machine learning classification

Cited by 3 publications

References 23 publications

Effectiveness of coronavirus disease-19 vaccination on disease transmission, hospitalization, and clinical outcomes in adults in North India

Effectiveness of coronavirus disease-19 vaccination on disease transmission, hospitalization, and clinical outcomes in adults in North India

A Comprehensive Review of Machine Learning Used to Combat COVID-19

Prediction of Type 2 Diabetes Mellitus Using Soft Computing

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