Determining the Efficiency of Drugs Under Special Conditions From Users’ Reviews on Healthcare Web Forums

Saad, Eysha; Din, Sadia; Jamil, Ramish; Rustam, Furqan; Mehmood, Arif; Ashraf, Imran; Choi, Gyu Sang

doi:10.1109/access.2021.3088838

Cited by 39 publications

(34 citation statements)

References 41 publications

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“…Experimental results reveal that the models perform better when used with TextBlob-annotated data as compared to VADER and AFINN. Previous studies [ 50 , 51 , 52 ] show that models perform better when trained on TextBlob labeled data, and this study confirms the same.…”

Section: Resultssupporting

confidence: 87%

COVID-19 Vaccination-Related Sentiments Analysis: A Case Study Using Worldwide Twitter Dataset

et al. 2022

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COVID-19 pandemic has caused a global health crisis, resulting in endless efforts to reduce infections, fatalities, and therapies to mitigate its after-effects. Currently, large and fast-paced vaccination campaigns are in the process to reduce COVID-19 infection and fatality risks. Despite recommendations from governments and medical experts, people show conceptions and perceptions regarding vaccination risks and share their views on social media platforms. Such opinions can be analyzed to determine social trends and devise policies to increase vaccination acceptance. In this regard, this study proposes a methodology for analyzing the global perceptions and perspectives towards COVID-19 vaccination using a worldwide Twitter dataset. The study relies on two techniques to analyze the sentiments: natural language processing and machine learning. To evaluate the performance of the different lexicon-based methods, different machine and deep learning models are studied. In addition, for sentiment classification, the proposed ensemble model named long short-term memory-gated recurrent neural network (LSTM-GRNN) is a combination of LSTM, gated recurrent unit, and recurrent neural networks. Results suggest that the TextBlob shows better results as compared to VADER and AFINN. The proposed LSTM-GRNN shows superior performance with a 95% accuracy and outperforms both machine and deep learning models. Performance analysis with state-of-the-art models proves the significance of the LSTM-GRNN for sentiment analysis.

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Section: Resultssupporting

confidence: 87%

COVID-19 Vaccination-Related Sentiments Analysis: A Case Study Using Worldwide Twitter Dataset

et al. 2022

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“…Learning algorithms are highly dependent on the features to make accurate predictions [ 43 ]. This makes extraction of features a very crucial step as highly correlated features will produce a better outcome and highly accurate classification and vice versa.…”

Section: Methodsmentioning

confidence: 99%

Analyzing preventive precautions to limit spread of COVID-19

Ahmad

Rustam²,

Saad³

et al. 2022

PLoS ONE

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With the global spread of COVID-19, the governments advised the public for adopting safety precautions to limit its spread. The virus spreads from people, contaminated places, and nozzle droplets that necessitate strict precautionary measures. Consequently, different safety precautions have been implemented to fight COVID-19 such as wearing a facemask, restriction of social gatherings, keeping 6 feet distance, etc. Despite the warnings, highlighted need for such measures, and the increasing severity of the pandemic situation, the expected number of people adopting these precautions is low. This study aims at assessing and understanding the public perception of COVID-19 safety precautions, especially the use of facemask. A unified framework of sentiment lexicon with the proposed ensemble EB-DT is devised to analyze sentiments regarding safety precautions. Extensive experiments are performed with a large dataset collected from Twitter. In addition, the factors leading to a negative perception of safety precautions are analyzed by performing topic analysis using the Latent Dirichlet allocation algorithm. The experimental results reveal that 12% of the tweets correspond to negative sentiments towards facemask precaution mainly by its discomfort. Analysis of change in peoples’ sentiment over time indicates a gradual increase in the positive sentiments regarding COVID-19 restrictions.

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“…The sigmoid function is an 'S'-shaped curve that restricts the probabilistic value between the discrete target variables. It works efficiently for classification tasks [42]. The logistic regression technique is an advanced method of linear regression used for both classification and prediction in complex linear and non-linear datasets.…”

Section: Plos Onementioning

confidence: 99%

Novel extreme regression-voting classifier to predict death risk in vaccinated people using VAERS data

et al. 2022

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COVID-19 vaccination raised serious concerns among the public and people are mind stuck by various rumors regarding the resulting illness, adverse reactions, and death. Such rumors are dangerous to the campaign against the COVID-19 and should be dealt with accordingly and timely. One prospective solution is to use machine learning-based models to predict the death risk for vaccinated people and clarify people’s perceptions regarding death risk. This study focuses on the prediction of the death risks associated with vaccinated people followed by a second dose for two reasons; first to build consensus among people to get the vaccines; second, to reduce the fear regarding vaccines. Given that, this study utilizes the COVID-19 VAERS dataset that records adverse events after COVID-19 vaccination as ‘recovered’, ‘not recovered’, and ‘survived’. To obtain better prediction results, a novel voting classifier extreme regression-voting classifier (ER-VC) is introduced. ER-VC ensembles extra tree classifier and logistic regression using soft voting criterion. To avoid model overfitting and get better results, two data balancing techniques synthetic minority oversampling (SMOTE) and adaptive synthetic sampling (ADASYN) have been applied. Moreover, three feature extraction techniques term frequency-inverse document frequency (TF-IDF), bag of words (BoW), and global vectors (GloVe) have been used for comparison. Both machine learning and deep learning models are deployed for experiments. Results obtained from extensive experiments reveal that the proposed model in combination with TF-TDF has shown robust results with a 0.85 accuracy when trained on the SMOTE-balanced dataset. In line with this, validation of the proposed voting classifier on binary classification shows state-of-the-art results with a 0.98 accuracy. Results show that machine learning models can predict the death risk with high accuracy and can assist the authors in taking timely measures.

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Determining the Efficiency of Drugs Under Special Conditions From Users’ Reviews on Healthcare Web Forums

Cited by 39 publications

References 41 publications

COVID-19 Vaccination-Related Sentiments Analysis: A Case Study Using Worldwide Twitter Dataset

COVID-19 Vaccination-Related Sentiments Analysis: A Case Study Using Worldwide Twitter Dataset

Analyzing preventive precautions to limit spread of COVID-19

Novel extreme regression-voting classifier to predict death risk in vaccinated people using VAERS data

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