Alleviation of Antimicrobial Therapy in Icu During Covid-19 Second Wave – A Review Paper

Nassef, Mohamed; Shorrab, Ahmed A.; Buheji, Mohamed; Buheji, Abdul Rahman; Abosamak, Mohammed Fawzi

doi:10.34218/ijm.11.12.2020.204

Cited by 3 publications

(2 citation statements)

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Section: Discussionmentioning

confidence: 99%

“…From those researches, this comparison table illustrates that the supervised learning plays a vital role in prediction using symptoms, clinical features and chest X-rays. Some researchers used classification model with different algorithms for example, shallow singlelayer perceptron neural network, Gaussian process regression, Bayes Net, logistic, J48, multinomial Naive Bayes classifier, support vector machine etc., [17][18][19][20][21][22][23][24] Some researchers used regression, recurrent neural network and convolutional neural network. From all the experiments and studies, it is noted that supervised learning techniques are alone used to obtain more accurate results.…”

Section: Discussionmentioning

confidence: 99%

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Comparative Study Based on Analysis of Coronavirus Disease (COVID-19) Detection and Prediction Using Machine Learning Models

Abirami

Kumar

2021

SN COMPUT. SCI.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Comparative Study Based on Analysis of Coronavirus Disease (COVID-19) Detection and Prediction Using Machine Learning Models

Abirami

Kumar

2021

SN COMPUT. SCI.

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Development and validation of a hybrid deep learning–machine learning approach for severity assessment of COVID-19 and other pneumonias

Park,

Jang,

Chung

et al. 2023

Sci Rep

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The Coronavirus Disease 2019 (COVID-19) is transitioning into the endemic phase. Nonetheless, it is crucial to remain mindful that pandemics related to infectious respiratory diseases (IRDs) can emerge unpredictably. Therefore, we aimed to develop and validate a severity assessment model for IRDs, including COVID-19, influenza, and novel influenza, using CT images on a multi-centre data set. Of the 805 COVID-19 patients collected from a single centre, 649 were used for training and 156 were used for internal validation (D1). Additionally, three external validation sets were obtained from 7 cohorts: 1138 patients with COVID-19 (D2), and 233 patients with influenza and novel influenza (D3). A hybrid model, referred to as Hybrid-DDM, was constructed by combining two deep learning models and a machine learning model. Across datasets D1, D2, and D3, the Hybrid-DDM exhibited significantly improved performance compared to the baseline model. The areas under the receiver operating curves (AUCs) were 0.830 versus 0.767 (p = 0.036) in D1, 0.801 versus 0.753 (p < 0.001) in D2, and 0.774 versus 0.668 (p < 0.001) in D3. This study indicates that the Hybrid-DDM model, trained using COVID-19 patient data, is effective and can also be applicable to patients with other types of viral pneumonia.

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Antibiotics Prescribing in Intensive Care Settings during the COVID-19 Era: A Systematic Review

Abu-Rub

Abdelrahman

Johar³

et al. 2021

Antibiotics

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The prevalence of patients admitted to intensive care units (ICUs) with SARS-CoV-2 infection who were prescribed antibiotics is undetermined and might contribute to the increased global antibiotic resistance. This systematic review evaluates the prevalence of antibiotic prescribing in patients admitted to ICUs with SARS-CoV-2 infection using PRISMA guidelines. We searched and scrutinized results from PubMed and ScienceDirect databases for published literature restricted to the English language up to 11 May 2021. In addition, we included observational studies of humans with laboratory-confirmed SARS-CoV-2 infection, clinical characteristics, and antibiotics prescribed for ICU patients with SARS-CoV-2 infections. A total of 361 studies were identified, but only 38 were included in the final analysis. Antibiotic prescribing data were available from 2715 patients, of which prevalence of 71% was reported in old age patients with a mean age of 62.7 years. From the reported studies, third generation cephalosporin had the highest frequency amongst reviewed studies (36.8%) followed by azithromycin (34.2%). The estimated bacterial infection in 12 reported studies was 30.8% produced by 15 different bacterial species, and S. aureus recorded the highest bacterial infection (75%). The fundamental outcomes were the prevalence of ICU COVID-19 patients prescribed antibiotics stratified by age, type of antibiotics prescribed, and the presence of co-infections and comorbidities. In conclusion, more than half of ICU patients with SARS-CoV-2 infection received antibiotics, and prescribing is significantly higher than the estimated frequency of identified bacterial co-infection.

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Alleviation of Antimicrobial Therapy in Icu During Covid-19 Second Wave – A Review Paper

Cited by 3 publications

References 0 publications

Comparative Study Based on Analysis of Coronavirus Disease (COVID-19) Detection and Prediction Using Machine Learning Models

Comparative Study Based on Analysis of Coronavirus Disease (COVID-19) Detection and Prediction Using Machine Learning Models

Development and validation of a hybrid deep learning–machine learning approach for severity assessment of COVID-19 and other pneumonias

Antibiotics Prescribing in Intensive Care Settings during the COVID-19 Era: A Systematic Review

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